Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-28T03:27:43.699Z Has data issue: false hasContentIssue false

Bibliography

Published online by Cambridge University Press:  23 May 2024

Wim Naudé ,
Thomas Gries  and
Nicola Dimitri
Wim Naudé
Affiliation:
Rheinisch-Westfälische Technische Hochschule, Aachen, Germany
Thomas Gries
Affiliation:
Universität Paderborn, Germany
Nicola Dimitri
Affiliation:
Università degli Studi, Siena
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Artificial Intelligence
Economic Perspectives and Models
 , pp. 315 - 353
Publisher: Cambridge University Press
Print publication year: 2024

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abbeel, P. and Ng, A. (2004). Apprenticeship Learning via Inverse Reinforcement Learning. Proceedings of the Twenty-First International Conference on Machine Learning, ICML 04. New York, USA.CrossRefGoogle Scholar
Acemoglu, D. (2002). Technical Change, Inequality, and the Labor Market. Journal of Economic Literature, 40(1):7–72.CrossRefGoogle Scholar
Acemoglu, D. (2009). Introduction to Modern Economic Growth. Princeton: Princeton University Press.Google Scholar
Acemoglu, D. and Autor, D. (2011). Skills, Tasks and Technologies: Implications for Employment and Earnings. In: Card, D. and Ashenfelter, O. (eds.) Handbook of Labor Economics. Amsterdam: Elsevier, pp. 1043–1171.Google Scholar
Acemoglu, D. and Autor, D. (2012). What Does Human Capital Do? A Review of Goldin and Katz’s The Race between Education and Technology. Journal of Economic Literature, 50(2):426–463.CrossRefGoogle Scholar
Acemoglu, D. and Restrepo, P. (2017). Robots and Jobs: Evidence from US Labor Markets. NBER Working Paper no. 23285. National Bureau for Economic Research.CrossRefGoogle Scholar
Acemoglu, D. and Restrepo, P. (2018a). Artificial Intelligence, Automation and Work. Working Paper no. 24196. National Bureau of Economic Research.CrossRefGoogle Scholar
Acemoglu, D. and Restrepo, P. (2018b). The Race between Man and Machine: Implications of Technology for Growth, Factor Shares and Employment. American Economic Review, 108(6):1488–1542.CrossRefGoogle Scholar
Acemoglu, D. and Restrepo, P. (2019a). Artificial Intelligence, Automation and Work. In: Argawal, A. K., Gans, J., and Goldfarb, A. (eds.) The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press, Chapter 8:197–236.CrossRefGoogle Scholar
Acemoglu, D. and Restrepo, P. (2019b). Automation and New Tasks: How Technology Displaces and Reinstates Labor. Journal of Economic Perspectives, 33(2):3–30.CrossRefGoogle Scholar
Acemoglu, D. and Restrepo, P. (2020). Robots and Jobs: Evidence from US Labor Markets. Journal of Political Economy, 128(6):2188–2244.CrossRefGoogle Scholar
Aghion, P., Dewatripont, M., and Rey, P. (1997). Corporate Governance, Competition Policy and Industrial Policy. European Economic Review, (41):797–805.CrossRefGoogle Scholar
Aghion, P., Jones, B., and Jones, C. (2017). Artificial Intelligence and Economic Growth. NBER Working Paper 23928, National Bureau for Economic Research.CrossRefGoogle Scholar
Agrawal, A., Gans, J., and Goldfarb, A. (2019a). Economic Policy for Artificial Intelligence. In: Lerner, J. and Stern, S. (eds.) Innovation Policy and the Economy, Vol. 19. NBER. pp. 139–159.Google Scholar
Agrawal, A., McHale, J., and Oettl, A. (2018). Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth. NBER Working Paper no. 24541. National Bureau for Economic Research.CrossRefGoogle Scholar
Agrawal, A., McHale, J., and Oettl, A. (2019b). Artificial Intelligence, Scientific Discovery, and Commercial Innovation. Mimeo. Published as NBER Working Paper number 31558, NBER: Cambridge MA.Google Scholar
Agrawal, S. (2019). Reinforcement Learning Lecture 1: Introduction. Minneapolis: University of Minnesota.Google Scholar
Ahiska, S., Appaji, S., King, R., and Warsing, D. Jr (2013). A Markov Decision Process-Based Policy Characterization Approach for a Stochastic Inventory Control Problem with Unreliable Sourcing. International Journal of Production Economics, 144(2):485–495.CrossRefGoogle Scholar
Ahuja, G. and Lampert, C. (2001). Entrepreneurship in the Large Corporation: A Longitudinal Study of How Established Firms Create Breakthrough Inventions. Strategic Management Journal, 22:521–543.CrossRefGoogle Scholar
AI and Inclusion Symposium (2017). An Evolving Reading List. Available at https://drive.google.com/file/d/0ByG0FdgytPz7YkxRMHF1d19ibE0/view.Google Scholar
AI Impacts (2016). Friendly AI as a Global Public Good. AI Impacts online, https://aiimpacts.org/friendly-ai-as-a-global-public-good/.Google Scholar
Aiello, L. (2016). The Multifaceted Impact of Ada Lovelace in the Digital Age. Artificial Intelligence, 236:58–62.CrossRefGoogle Scholar
Aldous, D. J. (2010). The Great Filter, Branching Histories and Unlikely Events. Mimeo: University of California, Berkeley.Google Scholar
Aleksander, S. (2019). 1960: The Year the Singularity Was Cancelled. Slate Star Codex Blog, April 22.Google Scholar
Alexander, S. (2016a). Ascended Economy? Star Slate Codex Blog, May 30.Google Scholar
Alexander, S. (2016b). Book Review: Age of EM. Slate Star Codex Blog, May 28.Google Scholar
Alexander, S. (2022). Book Review: What We Owe the Future. Astral Codex Ten, August 23.Google Scholar
Alkaissi, H. and McFarlane, S. (2023). Artificial Hallucinations in ChatGPT: Implications in Scientific Writing. Cureus, 15(2):e35179.Google ScholarPubMed
Allen, C., Smit, I., and Wallach, D. (2005). Artificial Morality: Top-Down, Bottom-Up, and Hybrid Approaches. Ethics and Information Technology, 7(3):149–155.CrossRefGoogle Scholar
Allen, P. and Greaves, M. (2011). The Singularity Isn’t Near. MIT Technology Review, October 12.Google Scholar
Amendola, G. (2023). Special Issue on Logic-Based Artificial Intelligence. Algorithms, 16(2):106.CrossRefGoogle Scholar
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D. (2016). Concrete Problems in AI Safety. ArXiv:1606.06565 [Cs], June: http://arxiv.org/abs/1606.06565.Google Scholar
Anderson, M. and Anderson, S. (2007). Machine Ethics: Creating an Ethical Intelligent Agent. AI Magazine, 28(4):15.Google Scholar
Ariely, D. (2009). Predictably Irrational: The Hidden Forces That Shape Our Decisions. London: HarperCollins.Google Scholar
Armstrong, S., Bostrom, N., and Schulman, C. (2016). Racing to the Precipice: A Model of Artificial Intelligence Development. AI & Society, 31:201–206.CrossRefGoogle Scholar
Armstrong, S. and Sandberg, A. (2013). Eternity in Six Hours: Intergalactic Spreading of Intelligent Life and Sharpening the Fermi Paradox. Acta Astronautica, 89:1–13.CrossRefGoogle Scholar
Armstrong, S., Sandberg, A., and Bostrom, N. (2012). Thinking Inside the Box: Controlling and Using an Oracle AI. Minds and Machines, 22(4):299–324.CrossRefGoogle Scholar
Armstrong, S. and Sotala, K. (2015). How We’re Predicting AI – or Failing to. In: Romportl, J., Zackova, E., and Kelemen, J. (eds.) Beyond Artificial Intelligence. Topics in Intelligent Engineering and Informatics, Vol. 9. Cham: Springer, pp. 11–29.Google Scholar
Arntz, M., Gregory, I., and Zierahn, U. (2017). Revisiting the Risk of Automation. Economic Letters, 159:157–160.CrossRefGoogle Scholar
Arrow, K. (1994). Methodological Individualism and Social Knowledge. American Economic Review, Papers and Proceedings, 84(2):1–9.Google Scholar
Arthur, W. (2021). Foundations of Complexity Economics. Nature Reviews Physics, 3:136–145.CrossRefGoogle ScholarPubMed
Arulkumaran, K., Deisenroth, M., Brundage, M., and Bharath, A. (2017). A Brief Survey of Deep Reinforcement Learning. arXiv:1708.05866v2 [cs.LG].Google Scholar
Aschenbrenner, L. (2020). Existential Risk and Growth. GPI Working Paper No. 6-2020, Global Priorities Institute, University of Oxford.Google Scholar
Aschhoff, B. and Sofka, W. (2009). Innovation on Demand: Can Public Procurement Drive Market Success of Innovations? Research Policy, 38(8):1235–1247.CrossRefGoogle Scholar
Ash, E. and Hansen, S. (2023). Text Algorithms in Economics. Annual Review of Economics, 15:659–688.CrossRefGoogle Scholar
Askell, A., Brundage, M., and Hadfield, G. (2019). The Role of Cooperation in Responsible AI Development. ArXiv, July.Google Scholar
Athey, S. and Imbens, G. (2019). Machine Learning Methods that Economists Should Know About. Annual Review of Economics, 11(1):685–725.CrossRefGoogle Scholar
Auerbach, D. (2014). The Most Terrifying Thought Experiment of All Time. Slate Magazine, 17 July.Google Scholar
Auerswald, P. E. (2017). The Code Economy: A Forty-thousand-year History. Oxford: Oxford University Press.Google Scholar
Autor, D. (2013). The “Task Approach” to Labour Markets. Journal for Labour Market Research, 46(3):185–199.CrossRefGoogle Scholar
Autor, D. (2014). Skills, Education, and the Rise of Earnings Inequality Among the Other 99 Percent. Science, 344(6186):843–851.CrossRefGoogle ScholarPubMed
Autor, D. and Dorn, D. (2013). The Growth of Low Skill Service Jobs and the Polarization of the US Labor Market. American Economic Review, 103(5):1553–1597.CrossRefGoogle Scholar
Autor, D., Dorn, D., Katz, L., Patterson, C., and Reenen., J. V. (2017). The Fall of the Labour Share and the Rise of Superstar Firms. Working Paper no. 23396. National Bureau of Economic Research.Google Scholar
Autor, D., Katz, L., and Krueger, A. (1999). Computing Inequality: Have Computers Changed the Labor Market? Quarterly Journal of Economics, 113(4):1169–1214.CrossRefGoogle Scholar
Autor, D., Levy, F., and Murnane, R. (2003). The Skill Content of Recent Technological Change: An Empirical Exploration. Quarterly Journal of Economics, 118(4):1279–1333.CrossRefGoogle Scholar
Autor, D. and Salomons, A. (2018). Is Automation Labor-displacing? Productivity Growth, Employment and the Labor Share. Brookings Papers on Economic Activity, BPEA Conference, 8–9 March.CrossRefGoogle Scholar
Azoulay, P., Fuchs, E., Goldstein, A., and Kearney, M. (2018a). Funding Breakthrough Research: Promises and Challenges of the ARPA Model. Innovation Policy and the Economy, 19:69–96.CrossRefGoogle Scholar
Azoulay, P., Jones, B., Kim, J., and Miranda, J. (2018b). Age and High-growth Entrepreneurship. NBER Working Paper Series no. 24489. National Bureau for Economic Research.CrossRefGoogle Scholar
Baker-Brunnbauer, J. (2020). Management Perspective of Ethics in Artificial Intelligence. AI and Ethics, 11 September. https://doi.org/10.1007/s43681–020–00022–3.Google Scholar
Baranzini, A. and Bourguignon, F. (1995). Is Sustainable Growth Optimal? International Tax and Public Finance, 2(2):341–356.CrossRefGoogle Scholar
Barnett, M. (2020). Distinguishing Definitions of Takeoff. AI Alignment Forum, 14 February.Google Scholar
Barnett, M. (2022). My Current Thoughts on the Risks from SETI. Effective Altruism Forum, 15 March.Google Scholar
Barrett, A. and Baum, S. (2017). A Model of Pathways to Artificial Superintelligence Catastrophe for Risk and Decision Analysis. Journal of Experimental and Theoretical Artificial Intelligence, 29(2):397–414.CrossRefGoogle Scholar
Barrett, S. (2007). Why Cooperate? The Incentive to Supply Global Public Goods. Oxford: Oxford University Press.CrossRefGoogle Scholar
Basuchoudhary, A., Bang, J., and Sen, T. (2017). Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth. Cham: Springer Publishing.CrossRefGoogle Scholar
Baum, S. D., Haqq-Misra, J., and Domagal-Goldman, S. (2011). Would Contact with Extraterrestrials Benefit or Harm Humanity? A Scenario Analysis. Acta Astronautica, 689(11–12):2144–2129.Google Scholar
Beard, S. and Torres, E. (2020). Ripples on the Great Sea of Life: A Brief History of Existential Risk Studies. Mimeo: SSRN, 12 March. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3730000.Google Scholar
Beaudry, P., Green, D., and Sand, B. (2013). The Great Reversal in the Demand for Skill and Cognitive Tasks. Working Paper No.18901. National Bureau of Economic Research.CrossRefGoogle Scholar
Beckstead, N. (2013). On the Overwhelming Importance of Shaping the Far Future. DPhil Thesis, Rutgers University.Google Scholar
Bellman, R. (1957a). Dynamic Programming. Princeton: Princeton University Press.Google ScholarPubMed
Bellman, R. (1957b). A Markovian Decision Process. Journal of Mathematics and Mechanics, 6(5):679–684.Google Scholar
Bennett, M. (2021). Compression, The Fermi Paradox and Artificial Superintelligence. In: Goertzel, B., Iklé, M., and Potapov, A. (eds.) Artificial General Intelligence. Lecture Notes in Computer Science, vol. 13154. Cham: Springer, pp. 41–44.Google Scholar
Benya (2012). Why You Must Maximise Expected Utility. AI Alignment Forum, 13 December.Google Scholar
Benzell, S., Kotlikoff, L., LaGarda, G., and Sachs, J. (2015). Robots Are Us: Some Economics of Human Replacement. NBER Working Paper no. 20941.CrossRefGoogle Scholar
Benzell, S., Kotlikoff, L., LaGardia, G., and Sachs, J. (2018). Robots are Us: Some Economics of Human Replacement. Working Paper 20941. National Bureau of Economic Research.Google Scholar
Beraja, M., Yang, D., and Yuchtman, N. (2020). Data-intensive Innovation and the State: Evidence from AI Firms in China. NBER Working Paper No. 27723, National Bureau of Economic Research.CrossRefGoogle Scholar
Berg, A., Buffie, E., and Zanna, L. (2018). Robots, Growth, and Inequality: Should We Fear the Robot Revolution? (The Correct Answer is Yes). IMF Working Paper no. 18/116.CrossRefGoogle Scholar
Bernheim, B. and Whinston, M. (1986). Common Agency. Econometrica, 54(4):923–942.CrossRefGoogle Scholar
Bernoulli, D. (1738). Commentarii. Academiae Scientiarum Imperialis Petropolitanae 5 175-192; English translation (1954). Econometrica, 22:23–36.Google Scholar
Bessen, J. (2018). AI and Jobs: The Role of Demand. NBER Working Paper no. 24235. National Bureau for Economic Research.CrossRefGoogle Scholar
Bessen, J., Goos, M., Salomons, A., and van den Berge, W. (2019). Automatic Reaction – What Happens to Workers at Firms that Automate? Law and Economics Series Paper no. 19-2, Boston University School of Law.Google Scholar
Biever, C. (2023). The Easy Intelligence Tests that AI Chatbots Fail. Nature, 619:686–689.CrossRefGoogle Scholar
Bilkic, N., Gries, T., and Naudé, W. (2013). The Radical Innovation Investment Decision Refined. IZA DP No. 7338, Bonn: IZA Institute of Labor Economics.Google Scholar
Bilkic, N., Gries, T., and Pilichowski, M. (2009). Stay in School or Start Working? The Human Capital Investment Decision Under Uncertainty and Irreversibility. CESifo Working Paper, No. 2825. Center for Economic Studies and ifo Institute (CESifo), Munich.Google Scholar
Binmore, K. (2007). Rational Decisions in Large Worlds. Annales d’Économie et de Statistique, 86:25–41.CrossRefGoogle Scholar
Binmore, K. (2008). Rational Decisions. Princeton: Princeton University Press.CrossRefGoogle Scholar
Binmore, K. (2017). On the Foundations of Decision Theory. Homo Oeconomicus, 34:259–273.CrossRefGoogle Scholar
Bishop, J. (2021). Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It. Frontiers of Psychology, 11:513474.CrossRefGoogle ScholarPubMed
Bloom, D., McKenna, M., and Prettner, K. (2018). Demography, Unemployment, Automation, and Digitalization: Implications for the Creation of (Decent) Jobs, 2010-2030. IZA Discussion Paper no. 11739. IZA Institute of Labor Economics, Bonn.Google Scholar
Bloom, N., Bunn, P., Chen, S., Mizen, P., and Smietanka, P. (2020). The Economic Impact of Coronavirus on UK Businesses: Early Evidence from the Decision Maker Panel. VOX CEPR Policy Portal, 27th March.Google Scholar
Bloom, N., van Reenen, J., and Williams, H. (2019). A Toolkit of Policies to Promote Innovation. Journal of Economic Perspectives, 33(3):163–184.CrossRefGoogle Scholar
Bloomberg (2018). The World’s Biggest AI Start-up Raises USD 1.2 Billion in Mere Months. Fortune Magazine, 31 May 2018.Google Scholar
Boddington, P. (2017). Towards A Code of Ethics for Artificial Intelligence. Heidelberg: Springer.CrossRefGoogle Scholar
Boden, M. (2014). GOFAI. In: Frankish, K. and Ramsey, W. (eds.), The Cambridge Handbook of Artificial Intelligence. Cambridge: Cambridge University Press, pp. 89–107.CrossRefGoogle Scholar
Bommasani, R., Hudson, D. A., and et al. (2022). On the Opportunities and Risks of Foundation Models. arXiv preprint arXiv:2108.07258.Google Scholar
Bostrom, N. (2002). Existential Risks: Analyzing Human Extinction Scenarios and Related Hazards. Journal of Evolution and Technology, 9(1).Google Scholar
Bostrom, N. (2003a). Astronomical Waste: The Opportunity Cost of Delayed Technological Development. Utilitas, 15(3):308–314.CrossRefGoogle Scholar
Bostrom, N. (2003b). Ethical Issues in Advanced Artificial Intelligence. In: Smit, I. et al. (eds.) Cognitive, Emotive and Ethical Aspects of Decision Making in Humans and in Artificial Intelligence, 2nd ed. International Institute of Advanced Studies in Systems Research and Cybernetics, pp. 12–17. Available at https://nickbostrom.com/ethics/ai.Google Scholar
Bostrom, N. (2006). What Is A Singleton? Linguistic and Philosophical Investigations, 5(2):48–54.Google Scholar
Bostrom, N. (2008). Where Are They? Why I Hope the Search for Extraterrestrial Life Finds Nothing. MIT Technology Review, May/June:72–77.Google Scholar
Bostrom, N. (2012). The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents. Minds and Machines, 22(2):71–85.CrossRefGoogle Scholar
Bostrom, N. (2013). Existential Risk Prevention as Global Priority. Global Policy, 4:15–31.CrossRefGoogle Scholar
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press.Google Scholar
Bostrom, N. (2017). Strategic Implications of Openness in AI Development. Global Policy, 8(2):135–148.CrossRefGoogle Scholar
Bowles, J. (2017). The Computerization of European Jobs. Bruegel.org, 24th July.Google Scholar
Bowles, S. and Choi, J.-K. (2019). The Neolithic Agricultural Revolution and the Origins of Private Property. Journal of Political Economy, 127(5):2186–2228.CrossRefGoogle Scholar
Bricker, D. and Ibbitson, J. (2020). Empty Planet: The Shock of Global Population Decline. New York: Crown Publishers. Little, Brown Book Group.Google Scholar
Brin, D. (2008). Shouting at the Cosmos: … or How SETI has Taken a Worrisome Turn Into Dangerous Territory. Lifeboat Foundation, July.Google Scholar
Brooks, R. (2017). The Seven Deadly Sins of AI Predictions. MIT Technology Review, 6 October.Google Scholar
Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., and et al. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. ArXiv [cs.CY], April: https://arxiv.org/abs/2004.07213.Google Scholar
Brynjolfsson, E. and McAfee, A. (2012). Thriving in the Automated Economy. The Futurist, 46(2):27–31.Google Scholar
Brynjolfsson, E. and McAfee, A. (2015). Will Humans Go the Way of Horses? Foreign Affairs, 94:8–14.Google Scholar
Brynjolfsson, E., Rock, D., and Syverson, C. (2017). Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. NBER Working Paper no. 24001. National Bureau for Economic Research.Google Scholar
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., and et al. (2023). Sparks of Artificial General Intelligence: Early Experiments with GPT-4. arXiv:2303.12712 [cs.CL].Google Scholar
Buchholz, W. and Schymura, M. (2012). Expected Utility Theory and the Tyranny of Catastrophic Risks. Ecological Economics, 77:234–239.CrossRefGoogle Scholar
Bushwick, S. and Leffer, L. (2023). The State of Large Language Models. Scientific American, 2 October.Google Scholar
Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constant, A., Deane, G., Fleming, S., Frith, C., Ji, X., and et al. (2023). Consciousness in Artificial Intelligence: Insights from the Science of Consciousness. arXiv:2308.08708 [cs.AI], 17 August.Google Scholar
Cai, N. and Kou, S. (2011). Option Pricing Under a Mixed-exponential Jump Diffusion Model. Management Science, 57(11):2067–2081.CrossRefGoogle Scholar
Calo, R. (2017). Artificial Intelligence Policy: A Primer and Roadmap. SSRN, At https://ssrn.com/abstract=3015350.Google Scholar
Camerer, C. (2019). Artificial Intelligence and Behavioral Economics. In: Agrawal, A., Gans, J., and Goldfarb, A. (eds.) The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press, pp. 587–610.Google Scholar
Caplin, A., Martin, D., and Marx, P. (2022). Modeling Machine Learning. NBER Working Paper No. 30600.Google Scholar
Carbonero, F., Ernst, E., and Weber, E. (2018). Robots Worldwide: The Impact of Automation on Employment and Trade. Working Paper no. 36, ILO Research Department.Google Scholar
Card, D. and DiNardo, J. (2002). Skill-biased Technological Change and Rising Wage Inequality: Some Problems and Puzzles. Journal of Labor Economics, 20(4):733–783.CrossRefGoogle Scholar
Carrigan Jr, R. (2006). Do Potential SETI Signals Need to be Decontaminated? Acta Astronaut, 58(2):112–117.CrossRefGoogle Scholar
Cassan, A., Kubas, D., Beaulieu, J., Dominik, M., Horne, K., Greenhill, J., and et al. (2012). One or More Bound Planets per Milky Way Star from Microlensing Observations. Nature, 481:167–169.CrossRefGoogle ScholarPubMed
Castelvecchi, D. (2016). The Black Box of AI. Nature, 538:21–23.CrossRefGoogle ScholarPubMed
Cerulli, G. (2021). Improving Econometric Prediction by Machine Learning. Applied Economics Letters, 28(16):1419–1425.CrossRefGoogle Scholar
Cerulli, G. (2022). Machine Learning using Stata/Python. The Stata Journal, 22(4):772–810.CrossRefGoogle Scholar
Cervantes, J., Lopez, L., Rodriguez, L., Cervantes, S., Cervantes, F., and Ramos, F. (2020). Artificial Moral Agents: A Survey of the Current Status. Science and Engineering Ethics, 26:501–532.CrossRefGoogle ScholarPubMed
Chace, C. (2020a). Artificial Intelligence and Fully Automated Luxury Capitalism. Forbes, 15 July.Google Scholar
Chace, C. (2020b). The Economic Singularity, 3rd ed. Three C’s.Google Scholar
Chalmers, D. (1995). Facing Up to the Problem of Consciousness. Journal of Consciousness Studies, 2(3):200–219.Google Scholar
Chalmers, D. (2010). The Singularity: A Philosophical Analysis. Journal of Consciousness Studies, 17(9):7–65.Google Scholar
Chan, F. and Mátyás, L. (2022). Econometrics with Machine Learning. Cham: Springer.CrossRefGoogle Scholar
Chandy, R. and Tellis, G. (1998). Organizing for Radical Product Innovation: The Overlooked Role of Willingness to Cannibalize. Journal of Marketing Research, 35(4):474–487.CrossRefGoogle Scholar
Charlesworth, A. (2014). The Comprehensibility Theorem and the Foundations of Artificial Intelligence. Minds and Machines, 24:439–476.CrossRefGoogle Scholar
Charpentier, A., Élie, E., and Remlinger, C. (2021). Reinforcement Learning in Economics and Finance. Computational Economics, 62:425–462.CrossRefGoogle Scholar
Chen, K., Raghupathi, S., Chandratreya, I., Du, Q., and Lipson, H. (2022). Automated Discovery of Fundamental Variables Hidden in Experimental Data. Nature Computational Science, 2:433–442.CrossRefGoogle ScholarPubMed
Chen, P. and Zhang, C. (2014). Data Intensive Applications, Challenges, Techniques and Technologies: A Survey on Big Data. Information Sciences, 275:314–347.CrossRefGoogle Scholar
Chiacchio, F., Petropoulos, G., and Pichler, D. (2018). The Impact of Industrial Robots on EU Employment and Wages: A Local Labor Market Approach. Working Paper no. 02, Bruegel, Brussels.Google Scholar
Christian, B. (2020). The Alignment Problem: Machine Learning and Human Values. New York: W.W. Norton.Google Scholar
Cirkovic, M. (2002). Cosmological Forecast and its Practical Significance. Journal of Evolution and Technology, 12(2).Google Scholar
Cirković, M. and Vukotić, B. (2008). Astrobiological Phase Transition: Towards Resolution of Fermi’s Paradox. Origins of Life and Evolution of Biospheres, 38(6):535–547.CrossRefGoogle ScholarPubMed
Clancy, M. (2021). Combinatorial Innovation and Technological Progress in the Very Long Run. New Things Under the Sun, June 18.Google Scholar
Clarke, R. (2015). Big Data Big Risks. Information Systems Journal, 26(1):77–90.CrossRefGoogle Scholar
Clemens, J. and Rogers, P. (2020). Demand Shocks, Procurement Policies, and the Nature of Medical Innovation: Evidence from Wartime Prosthetic Device Patents. NBER Working Paper No. 2667, National Burea of Economic Research.Google Scholar
Cockburn, I., Henderson, R., and Stern, S. (2017). The Impact of Artificial Intelligence on Innovation. Paper presented at the NBER Conference on Research Issues in Artificial Intelligence, Toronto, September.Google Scholar
Cockburn, I. M., Henderson, R., and Stern, S. (2019). The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis. In: Agrawal, A., Gans, J., and Goldfarb, A. (eds.) The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press, pp. 115–148.CrossRefGoogle Scholar
Cohen, M., Hutter, M., and Osborne, M. (2022). Advanced Artificial Agents Intervene in the Provision of Reward. AI Magazine, 43(3):282–293.CrossRefGoogle Scholar
Colbrook, M., Antun, V., and Hansen, A. (2022). The Difficulty of Computing Stable and Accurate Neural Networks: On the Barriers of Deep Learning and Smales 18th Problem. PNAS, 119(12):e2107151119.CrossRefGoogle Scholar
Comin, D. and Mestieri, M. (2013). If Technology has Arrived Everywhere, Why has Income Diverged? NBER Working Paper No. 19010. National Bureau for Economic Research.Google Scholar
Conselice, C., Wilkinson, A., Duncan, K., and Mortlock, A. (2016). The Evolution of Galaxy Number Density at z < 8 and its Implications. Astrophysical Journal, 830(2):1–17.CrossRefGoogle Scholar
Cook, T. (2014). Big Data in Research on Social Policy. Journal of Policy Analysis and Management, 33(2):544–547.CrossRefGoogle Scholar
Cords, D. and Prettner, K. (2019). Technological Unemployment Revisited: Automation in a Search and Matching Framework. GLO Discussion Paper no. 308, Global Labor Organization.Google Scholar
Cotra, A. (2020). Draft Report on AI Timelines. AI Alignment Forum, 19 September.Google Scholar
Cowen, T. (2010). The Great Stagnation. New York: Penguin (Dutton).Google Scholar
Cox, H., Fairchild, J., and Pedersen, H. (2004). Valuation of Structured Risk Management Products. Insurance, Mathematics and Economics, 34(2):259–272.CrossRefGoogle Scholar
Cox, S. and Pedersen, H. (2000). Catastrophe Risk Bonds. North American Actuarial Journal, 4(4):56–82.CrossRefGoogle Scholar
Crawford, J. (2022). Can Economic Growth Continue Over the Long-term? Longnow, 7 October.Google Scholar
Crawford, K. and Calo, R. (2016). There Is a Blind Spot in AI Research. Nature, 538:311–313.CrossRefGoogle ScholarPubMed
Crespi, F. and Guarascio, D. (2019). The Demand-Pull Effect of Public Procurement on Innovation and Industrial Renewal. Industrial and Corporate Change, 28(4):793–815.CrossRefGoogle Scholar
Czarnitzki, D., Hünermund, P., and Moshgbar, N. (2020). Public Procurement of Innovation: Evidence from a German Legislative Reform. International Journal of Industrial Organization, 71:1–16.CrossRefGoogle Scholar
Dafoe, A. (2018). AI Governance: A Research Agenda. Centre for the Governance of AI, University of Oxford.Google Scholar
Dafoe, A. (2020). AI Governance: Opportunity and Theory of Impact. Effective Altruism Forum, 17th September.Google Scholar
Dai, J. (2023). The Artificiality of Alignment. The Gradient, 7 October.Google Scholar
Dai, W. (2019). AGI will Drastically Increase Economies of Scale. AI Alignment Forum, 8 June.Google Scholar
Daneke, G. A. (2020). Machina-Economicus or Homo-Complexicus: Artificial Intelligence and the Future of Economics? Real-World Economics Review, 93:18–39.Google Scholar
Dasgupta, P. (1986). The Theory of Technological Competition. In: Stiglitz, J. and Mathewson, G. F. (eds.) New Developments in the Analysis of Market Structure. Cambridge: MIT Press. pp. 519–547.CrossRefGoogle Scholar
Dastani, M., Hulstijn, J., and van der Torre, L. (2005). How to Decide What to Do? European Journal of Operational Research, 160:762–784.CrossRefGoogle Scholar
Davidson, T. (2021). Report on Whether AI Could Drive Explosive Economic Growth. Open Philantrophy, 17 June.Google Scholar
Davies, D. (1999). The Bombe: A Remarkable Logic Machine. Cryptologia, 23(2):108–138.CrossRefGoogle Scholar
Davies, J. (2017). Hidden AI – Ghosts In The Machine. Becoming Human: Artificial Intelligence Magazine, Sept 6.Google Scholar
Dawkins, R. (1976). The Selfish Gene. Oxford: Oxford University Press.Google Scholar
De Luna, P., Wei, J., Bengio, Y., Aspuru-Guzik, A., and Sargent, E. (2017). Use Machine Learning to Find Energy Materials. Nature, 552(7683):23–25.CrossRefGoogle ScholarPubMed
De Visscher, A. (2020). Artificial versus Biological Intelligence in the Cosmos: Clues from a Stochastic Analysis of the Drake Equation. International Journal of Astrobiology, 19:353–359.CrossRefGoogle Scholar
de Vries, A. (2023). The Growing Energy Footprint of Artificial Intelligence. Joule. https://doi.org/10.1016/j.joule.2023.09.004.Google Scholar
Deisenroth, M., Faisal, A., and Ong, C. (2020). Mathematics for Machine Learning. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
DeLong, J. B. (1998). Estimates of World GDP, One Million B.C. – Present. Department of Economics, U.C. Berkeley at http://econ161.berkeley.edu/.Google Scholar
Deming, D. (2015). The Growing Importance of Social Skills in the Labor Market. Working Paper no. 21473. National Bureau of Economic Research.CrossRefGoogle Scholar
Dencik, L., Hintz, A., Redden, J., and Treré, E. (2019). Exploring Data Justice: Conceptions, Applications and Directions. Information, Communication and Society, 22(7):873–881.CrossRefGoogle Scholar
Dennis, L. (2020). Computational Goals, Values and Decision-making. Science and Engineering Ethics, 26(5):2487–2495.CrossRefGoogle ScholarPubMed
Deutsch, D. (2011). The Beginning of Infinity: Explanations that Transforms the World. London: Penguin Books.Google Scholar
Diamond, J. (1999). To Whom it May Concern. New York Times Magazine, 5 December:68–71.Google Scholar
Diebold, F. (2012). A Personal Perspective on the Origin(s) and Development of Big Data: The phenomenon, the Term, and the Discipline. Mimeo: University of Pennsylvania.Google Scholar
Ding, Y., Florensa, C., Phielipp, M., and Abbeel, P. (2019). Goal-conditioned Imitation Learning. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.Google Scholar
Dixit, A. (1989). Entry and Exit Decisions under Uncertainty. Journal of Political Economy, 97(3):620–638.CrossRefGoogle Scholar
Dixit, A. (1993). The Art of Smooth Pasting. Chur, Switzerland: Harwood Academic Publishers.Google Scholar
Dixon, H. (2001). Surfing Economics. London: Red Globe Press.CrossRefGoogle Scholar
Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine will Remake our World. London: Penguin Books.Google Scholar
Doraszelski, U. (2001). The Net Present Value Method versus the Option Value of Waiting: A Note on Farzin, Huisman and Kort (1998). Journal of Economic Dynamics and Control, 25(8):1109–1115.CrossRefGoogle Scholar
Drake, F. (1965). The Radio Search for Intelligent Extraterrestrial Life. In: Mamikunian, G. and Briggs, M. H. (eds.) Current Aspects of Exobiology. New York: Pergamon, pp. 323–345.CrossRefGoogle Scholar
Dubey, R., Agrawal, P., Pathak, D., Griffiths, T., and Efros, A. (2018). Investigating Human Priors for Playing Video Games. Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden.Google Scholar
Durkin, J. (1996). Expert Systems: A View of the Field. IEEE Intelligent Systems, 11(2):56–63.Google Scholar
Dutil, Y. and Dumas, S. (2007). Sustainability: A Tedious Path to Galactic Colonization. ArXiv:0711.1777 [physics.pop-ph].Google Scholar
Dutt, A. (2006). Aggregate Demand, Aggregate Supply and Economic growth. International Review of Applied Economics, 20(3):319–336.CrossRefGoogle Scholar
EC (2014). On the Coordination of Procedures for the Award of Public Works Contracts, Public Supply Contracts and Public Service Contracts. European Commission Directive 24. Brussels.Google Scholar
EC (2019). Ethics Guidelines for Trustworthy AI: High-level Expert Group on Artificial Intelligence. Brussels: European Commission.Google Scholar
Eckersley, P. (2019). Impossibility and Uncertainty Theorems in AI Value Alignment (or Why Your AGI Should Not Have a Utility Function). SafeAI 2019: Proceedings of the AAAI Workshop on Artificial Intelligence Safety 2019.Google Scholar
Edler, J. and Fagerberg, J. (2017). Innovation Policy: What, Why, and How. Oxford Review of Economic Policy, 33(1):2–23.CrossRefGoogle Scholar
Edler, J. and Georghiou, L. (2007). Public Procurement and Innovation – Resurrecting the Demand Side. Research Policy, 36(7):949–963.CrossRefGoogle Scholar
Ehrlich, P. (1968). The Population Bomb. New York: Ballantine Books.Google Scholar
Eisenstein, M. (2023). A Test of Artificial Intelligence. Nature, 14 September.CrossRefGoogle ScholarPubMed
Eitel-Porter, R. (2020). Beyond the Promise: Implementing Ethical AI. AI and Ethics, 6 October. https://doi.org/10.1007/s43681-020-00011-6.Google Scholar
Ellsberg, D. (1961). Risk, Ambiguity and the Savage Axioms. Quarterly Journal of Economics, 75(4):643–699.CrossRefGoogle Scholar
Eloundou, T., Manning, S., Mishkin, P., and Rock, D. (2023). Gpts are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. arXiv preprint arXiv:2303.10130.Google Scholar
Emani, C., Cullot, N., and Nicolle, C. (2015). Understandable Big Data: A Survey. Computer Science Review, 17:70–81.CrossRefGoogle Scholar
Emba, C. (2022). Why Llongtermism Isn’t Ethically Sound. Washington Post, 5 September.Google Scholar
Englander, A. (2021). How Would the Scaling Hypothesis Change Things? Less Wrong Blog, 13 August.Google Scholar
Erdil, E. and Besiroglu, T. (2023). Explosive Growth from AI Automation: A Review of the Arguments. arXiv:2309.11690.Google Scholar
Espinilla, M., Villarreal, V., and McChesney, I. (2019). Ubiquitous Computing and Ambient Intelligence – UCAmi. Sensors (Basel), 19(18):4034.CrossRefGoogle ScholarPubMed
Etzioni, A. and Etzioni, O. (2017). Incorporating Ethics into Artificial Intelligence. Journal of Ethics, 21(4):403–418.CrossRefGoogle Scholar
European Commission (2007). Pre-commercial Procurement: Driving Innovation to Ensure Sustainable High Quality Public Services in Europe. Brussels: EC Communication 799.Google Scholar
European Commission (2014). European Directive on Public Procurement and Repealing Directive 2004/18/ec. Brussels: EC.Google Scholar
European Commission (2020). European Enterprise Survey on the Use of Technologies based on Artificial Intelligence: Final Report. Publications Office, Directorate-General for Communications Networks, Content and Technology.Google Scholar
Everitt, T. and Hutter, M. (2016). Avoiding Wireheading with Value Reinforcement Learning. arXiv:1605.03143v1 [cs.AI].Google Scholar
Everitt, T., Lea, G., and Hutter, M. (2018). AGI Safety Literature Review. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), pp. 5441–5449.CrossRefGoogle Scholar
Farboodi, M., Mihet, R., Philippon, T., and Veldkamp, L. (2019). Big Data and Firm Dynamics. Working Paper no. 25515, National Bureau of Economic Research.CrossRefGoogle Scholar
Farzin, Y., Huismann, K., and Kort, P. (1998). Optimal Timing of Technology Adaption. Journal of Dynamics and Control, 22(5):779–799.CrossRefGoogle Scholar
Favaretto, M., Clercq, De, E., Schneble, C.O, and Elger, B. S. (2020). What Is Your Definition of Big Data? Researchers’ Understanding of the Phenomenon of the Decade. PLoS One, 15(2):e0228987.CrossRefGoogle ScholarPubMed
Fazekas, M., Toth, I., and King, L. (2016). An Objective Corruption Risk Index Using Public Procurement Data. European Journal of Criminal Policy and Research, 22(3):369–397.CrossRefGoogle Scholar
Fernandez-Villaverde, J. (2020). Simple Rules for a Complex World with Artificial Intelligence. PIER Working Paper No. 20-010.CrossRefGoogle Scholar
Ferrari, F., van Dijck, J., and van den Bosch, A. (2023). Foundation Models and the Privatization of Public Knowledge. Nature Machine Intelligence, 5(8):818–820.CrossRefGoogle Scholar
Feuerriegel, S., Dolata, M., and Schwabe, G. (2020). Fair AI: Challenges and Opportunities. Business and Information Systems Engineering, 62(4):379–384.CrossRefGoogle Scholar
Feuerriegel, S., Hartmann, J., Janiesch, C., Zschech, P., and et al. (2023). Generative AI. Business and Information Systems Engineering, 12 September [Early Online].Google Scholar
Finley, K. (2015). Tech Time Warp of the Week: Return to 1974, When a Computer Ordered a Pizza for the First Time. Wired, 30 January.Google Scholar
Floridi, L. (2018). The Ethics of Artificial Intelligence. In Franklin, D. (ed.) Megatech: Technology in 2050. London: Profile Books. pp. 155–163.Google Scholar
Floridi, L. (2020). AI and Its New Winter: From Myths to Realities. Philosophy and Technology, 33(1):1–3.CrossRefGoogle Scholar
Floridi, L. (2022). Ultraintelligent Machines, Singularity, and Other Sci-fi Distractions about AI. Mimeo: SSRN.Google Scholar
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., and Vayena, E. (2018). AI4People – An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4):689–707.CrossRefGoogle ScholarPubMed
Ford, M. (2016). The Rise of the Robots: Technology and the Threat of Mass Unemployment. London: Oneworld Publications.Google Scholar
Fornaro, P. and Luomaranta, H. (2020). Nowcasting Finnish Real Economic Activity: A Machine Learning Approach. Empirical Econonomics, 58(1):55–71.CrossRefGoogle Scholar
Fouse, S., Cross, S., and Lapin, Z. (2020). DARPA’s Impact on Artificial Intelligence. AI Magazine, Summer:3–9.Google Scholar
Freeman, C. (1994). The Economics of Technical Change. Cambridge Journal of Economics, 18(5):463–514.CrossRefGoogle Scholar
Frey, C. and Osborne, M. (2013). The Future of Employment: How Susceptible are Jobs to Computerization? Oxford Martin Programme on the Impacts of Future Technology, University of Oxford.Google Scholar
Frey, C. and Osborne, M. (2017). The Future of Employment: How Susceptible Are Jobs to Computerization? Technological Forecasting and Social Change, 114:254–280.CrossRefGoogle Scholar
Friedman, T. (2016). Thank You for Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations. New York: Picador.Google Scholar
Fullarton, C. (2016). Life-Altered Cosmologies. CQG+, 27 January.Google Scholar
Furman, J. and Seamans, R. (2019). AI and the Economy. In: Lerner, J. and Stern, S. (eds.) Innovation Policy and the Economy, Vol. 19. NBER. pp. 161–191.Google Scholar
G-20 (2019). G20 Ministerial Statement on Trade and Digital Economy. 9 May.Google Scholar
Gabriel, I. and Ghazavi, V. (2021). The Challenge of Value Alignment: From Fairer Algorithms to AI Safety. arXiv:2101.06060 [cs.CY].Google Scholar
Gale, J., Wandel, A., and Hill, H. (2020). Will Recent Advances in AI Result in a Paradigm Shift in Astrobiology and SETI? International Journal of Astrobiology, 19(3):295–298.CrossRefGoogle Scholar
Galera, E., GR, G. G., and Kinouchi, O. (2019). Invasion Percolation Solves Fermi Paradox but Challenges SETI Projects. International Journal of Astrobiology, 18(4):316–322.CrossRefGoogle Scholar
Gallagher, B. (2018). Scary AI Is More Fantasia than Terminator. Nautilus, 15 March. Available at http://nautil.us/issue/58/self/scary-ai-is-more-fantasia-than-terminator (Accessed 1 August 2018)Google Scholar
Gallier, J. and Quaintance, J. (2022). Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning. Mimeo: University of Pennsylvania.Google Scholar
Galor, O. and Weil, D. N. (2000). Population, Technology, and Growth: From Malthusian Stagnation to the Demographic Transition and Beyond. American Economic Review, 90(4):806–828.CrossRefGoogle Scholar
Gandomi, A. and Haider, M. (2015). Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management, 35(2):137–144.CrossRefGoogle Scholar
Gans, J. (2018). AI and the Paperclip Problem. VoxEU Column, 10 June.Google Scholar
García-García, J., García-Ródenas, R., López-Gómez, J., and Martín-Baos, J. (2022). A Comparative Study of Machine Learning, Deep Neural Networks and Random Utility Maximization Models for Travel Mode Choice Modelling. Transportation Research Procedia, 62:374–382.CrossRefGoogle Scholar
Gauchon, R. and Barigou, K. (2021). Expected Utility Maximization with Stochastically Ordered Returns. Mimeo: University of Lyon.Google Scholar
Gentzkow, M., Kelly, B., and Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3):535–574.CrossRefGoogle Scholar
Gerdon, S. and Molinari, V. (2020). How Governments Can Use Public Procurement to Shape the Future of AI Regulation – and Boost Innovation and Growth. World Economic Forum Blog, 8 June.Google Scholar
Geroski, P. (1990). Innovation, Technological Opportunity, and Market Structure. Oxford Economic Papers, 42(3):586–602.CrossRefGoogle Scholar
Gilboa, I. (2013). Theory of Decision under Uncertainty. Cambridge: Cambridge University Press.Google Scholar
Goertzel, B. (2012). Should Humanity Build a Global AI Nanny to Delay the Singularity Until It’s Better Understood? Journal of Consciousness Studies, 19(1):96–111.Google Scholar
Gogas, P. and Papadimitriou, T. (2021). Machine Learning in Economics and Finance. Computational Economics, 57(1):1–4.CrossRefGoogle Scholar
Golder, P., Shacham, R., and Mitra, D. (2009). When, By Whom, and How Are Radical Innovations Developed? Marketing Science, 28(1):166–179.CrossRefGoogle Scholar
Goldfarb, A. and Lindsay, J. (2022). Prediction and Judgment: Why Artificial Intelligence Increases the Importance of Humans in War. International Security, 46(3):7–50.CrossRefGoogle Scholar
Goldin, C. and Katz, L. (2010). The Race between Education and Technology. Cambridge: Harvard University Press.CrossRefGoogle Scholar
Gonzales, C. and Perny, P. (2020). Decision under Uncertainty. In: Marquis, P., Papini, O., and Prade, H. (eds.). A Guided Tour of Artificial Intelligence Research, Vol. I, Cham: Springer, pp. 549–586.CrossRefGoogle Scholar
Good, I. J. (1965). Speculations Concerning the First Ultraintelligent Machine. Advances in Computers, 6:31–88.CrossRefGoogle Scholar
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 2:2672–2680.Google Scholar
Goos, M. and Manning, A. (2007). Lousy and Lovely Jobs: The Rising Polarization of Work in Britain. Review of Economics and Statistics, 89(1):118–133.CrossRefGoogle Scholar
Government of Ireland. (2021). AI – Here for Good. A National Artificial Intelligence Strategy for Ireland. Available at https://enterprise.gov.ie/en/publications/publication-files/national-ai-strategy-executive-summary.pdf (Accessed 12 January 2024).Google Scholar
Gray, R. (2015). The Fermi Paradox is Neither Fermi’s nor a Paradox. Astrobiology, 15(3):195–199.CrossRefGoogle ScholarPubMed
Gregory, T., Salomons, A., and Zierahn, U. (2019). Racing with or Against the Machine? Evidence from Europe. IZA Discussion Paper no. 12063. IZA Institute of Labor Economics, Bonn.Google Scholar
Gries, T. (2020a). Income Polarization and Stagnation in a Stochastic Model of Growth: When the Demand Side Matters. CIE Working Papers No. 132, Paderborn University.Google Scholar
Gries, T. (2020b). A New Theory of Demand-restricted Growth: The Basic Idea. The American Economist, 65(1):11–27.CrossRefGoogle Scholar
Gries, T. and Naudé, W. (2011). Entrepreneurship and Human Development: A Capability Approach. Journal of Public Economics, 95(3–4):216–224.CrossRefGoogle Scholar
Gries, T. and Naudé, W. (2018). Artificial Intelligence, Jobs, Inequality and Productivity: Does Aggregate Demand Matter? IZA Discussion Paper no. 12005.Google Scholar
Gries, T. and Naudé, W. (2020). Artificial Intelligence, Income Distribution and Economic Growth. IZA Discussion Paper no. 13606.CrossRefGoogle Scholar
Gries, T. and Naudé, W. (2021). Modelling Artificial Intelligence in Economics. IZA Discussion Paper no. 14171, IZA Institute of Labor Economics.CrossRefGoogle Scholar
Gries, T. and Naudé, W. (2022). Artificial Intelligence in Economics. Journal for Labour Market Research, 56(12):1–13.CrossRefGoogle Scholar
Gross, D. and Sampat, B. (2020). Organizing Crisis Innovation: Lessons from World War II. NBER Working Paper No. 27909. National Bureau for Economic Research.Google Scholar
Grossman, S. and Hart, O. (1983). An Analysis of the Principal-Agent Problem. Econometrica, 51(1):7–45.CrossRefGoogle Scholar
Guo, I., Langrené, N., Loeper, G., and Ning, W. (2020). Robust Utility Maximization Under Model Uncertainty via a Penalization Approach. arXiv:1907.13345v5 [math.OC].Google Scholar
Gurnee, W. and Tegmark, M. (2023). Language Models Represent Space and Time. arXiv:2310.02207 [cs.LG].Google Scholar
Gurzadyan, V. and Penrose, R. (2016). CCC and the Fermi Paradox. The European Physics Journal Plus, 131:11.CrossRefGoogle Scholar
Hadfield-Menell, D., Dragan, A., Abbeel, P., and Russell, S. (2016). Cooperative Inverse Reinforcement Learning. arXiv:1606.03137 [cs.AI].Google Scholar
Hagendorff (2020). The Ethics of AI Ethics: An Evaluation of Guidelines. Minds & Machines, 30(1):99–120.CrossRefGoogle Scholar
Hammerman, R. and Russell, A. (2015). Ada’s Legacy: Cultures of Computing from the Victorian to the Digital Age. New York: Association for Computing Machinery and Morgan & Claypool.CrossRefGoogle Scholar
Hanson, R. (1998). The Great Filter – Are We Almost Past It? Mimeo, September 15.Google Scholar
Hanson, R. (2000). Long-term Growth as a Sequence of Exponential Modes. Mimeo: George Mason University.Google Scholar
Hanson, R. (2009). Limits to Growth. Overcoming Bias Blog.Google Scholar
Hanson, R. (2014). I Still Don’t Get Foom. Overcoming Bias Blog, 24 July.Google Scholar
Hanson, R. (2018). The Age of Em: Work, Love, and Life When Robots Rule the Earth. Oxford: Oxford University Press.Google Scholar
Hanson, R. (2020). How Far To Grabby Aliens? Part 1. Overcoming Bias Blog, 21 December.Google Scholar
Hanson, R. (2022). Macaskill on Value Lock-In. Overcoming Bias Blog, 16 August.Google Scholar
Hanson, R., Martin, D., McCarter, C., and Paulson, J. (2021). If Loud Aliens Explain Human Earliness, Quiet Aliens Are Also Rare. arXiv:2102.01522v3 [q-bio.OT].Google Scholar
Hanson, R. and Yudkowsky, E. (2013). The Hanson-Yudkowsky AI-Foom Debate. Machine Intelligence Research Institute, Berkeley 94704.Google Scholar
Hao, K. (2019). We Analyzed 16,625 Papers to Figure Out Where AI Is Headed Next. MIT Technology Review, 25 January.Google Scholar
Harré, M. (2021). Information Theory for Agents in Artificial Intelligence, Psychology, and Economics. Entropy, 23(3):310.CrossRefGoogle ScholarPubMed
Harris, S. (2015). Can We Avoid a Digital Apocalypse? SAM Harris Blog, 16 January. https://samharris.org/can-we-avoid-a-digital-apocalypse/.Google Scholar
Harris, S. (2020). Making Sense: Conversations on Consciousness, Morality and the Future of Humanity. London: Penguin.Google Scholar
Harsanyi, J. (1978). Bayesian Decision Theory and Utilitarian Ethics. American Economic Review, 68(2):223–228.Google Scholar
Hart, M. (1975). An Explanation for the Absence of Extraterrestrials on Earth. Quarterly Journal of The Royal Astronomical Society, 16:128–135.Google Scholar
Hauer, T. (2022). Importance and Limitations of AI Ethics in Contemporary Society. Humanities and Social Sciences Communications, 9(272):1–8.CrossRefGoogle Scholar
Hayter, C. S., Link, A., and Scott, J. (2018). Public-Sector Entrepreneurship. Oxford Review of Economic Policy, 34(4):676–694.CrossRefGoogle Scholar
Heaven, D. (2019). Deep Trouble for Deep Learning. Nature, 574(7777):163–166.CrossRefGoogle Scholar
Hémous, D. and Olsen, M. (2018). The Rise of the Machines: Automation, Horizontal Innovation and Income Inequality. Mimeo: University of Zurich.Google Scholar
Hendler, J. (2008). Avoiding Another AI Winter. IEEE Intelligent Systems, 23(2):2–4.Google Scholar
Herfeld, C. (2020). The Diversity of Rational Choice Theory: A Review Note. Topoi, 39(4):329–347.CrossRefGoogle Scholar
Hernández-Orallo, J. (2017). Evaluation in Artificial Intelligence: From Task-oriented to Ability-oriented Measurement. Artificial Intelligence Review, 48(3):397–447.CrossRefGoogle Scholar
Hess, A. (2022). Apocalypse Nowadays: The New Wave of Films About the End of the World. The Guardian, 3 January.Google Scholar
Hibbard, B. (2012). Model-based Utility Functions. Journal of Artificial General Intelligence, 3(1):1–24.CrossRefGoogle Scholar
Hickman, J. (1999). The Political Economy of Very Large Space Projects. Journal of Evolution and Technology, 4(1):1–14.Google Scholar
Hidalgo, C. (2015). Why Information Grows. London: Penguin.Google Scholar
Hilbert, M. (2016). Big Data for Development: A Review of Promises and Challenges. Development Policy Review, 34(1):135–174.CrossRefGoogle Scholar
Hill, C. and Rothaermel, F. (2003). The Performance of Incumbent Firms in the Face of Radical Technological Innovation. Academy of Management Review, 28(2):257–274.CrossRefGoogle Scholar
Hilton, B. (2022). Preventing an AI-related Catastrophe: AI Might Bring Huge Benefits – If We Avoid the Risks. 8,000 Hours, 25 August.Google Scholar
Hinton, G. and Salakhutdinov, R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786):504–507.CrossRefGoogle ScholarPubMed
Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18(7):1527–1554.CrossRefGoogle ScholarPubMed
Hollings, C., Martin, U., and Rice, A. (2018). Ada Lovelace: The Making of a Computer Scientist. Oxford: Bodleian Leibrary.Google Scholar
Hopenhayn, H., Neira, J., and Singhania, R. (2018). From Population Growth to Firm Demographics: Implications from Concentration, Entrepreneurship and the Labor Share. NBER Working Paper no. 25382.CrossRefGoogle Scholar
House of Lords (2018). AI in the UK: Ready, Willing and Able? Select Committee on Artificial Intelligence, HL Paper 100.Google Scholar
Howard, R. (1960). Dynamic Programming and Markov Processes. Cambridge: The MIT Press.Google Scholar
Hrala, J. (2016). Stephen Hawking Warns Us to Stop Reaching Out to Aliens Before It’s Too Late. Science Alert, 4 November.Google Scholar
Hsu, J. (2022). A Third of Scientists Working on AI Say It Could Cause Global Disaster. New Scientist, 20 September.Google Scholar
Huang, M.-H. and Rust, R. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2):155–172.CrossRefGoogle Scholar
Hubinger, E. (2020). An Overview of 11 Proposals for Building Safe Advanced AI. arXiv:2012.07532 [cs.LG].Google Scholar
Hudson, A., Finn, E., and Wylie, R. (2023). What Can Science Fiction Tell Us about the Future of Artificial Intelligence Policy? AI and Society, 38(1):197–211.CrossRefGoogle Scholar
Huebner, J. (2005). A Possible Declining Trend for Worldwide Innovation. Technological Forecasting and Social Change, 72(8):980–986.CrossRefGoogle Scholar
Humphries, E., Wright, C., Hoffman, A., Savonen, C., and Leek, J. (2023). What’s the Best Chatbot for Me? Researchers put LLMs Through their Paces. Nature, 27 September.CrossRefGoogle ScholarPubMed
Hutter, M. (2000). A Theory of Universal Artificial Intelligence Based on Algorithmic Complexity. arXiv:cs/0004001 [cs.AI].Google Scholar
Hutter, M. (2007). Universal Algorithmic Intelligence: A Mathematical Top-down Approach. In: Goertzel, B. and Pennachin, C. (eds.) Artificial General Intelligence. Cognitive Technologies. Berlin, Heidelberg: Springer, pp. 227–290.CrossRefGoogle Scholar
Huxley, A. (1932). Brave New World. London: Chatto & Windus.Google Scholar
IEEE. (2019). Ethically Aligned Design. 1st ed. Piscataway: The IEEE Global Initiative. https://ethicsinaction.ieee.org.Google Scholar
Jang, J. (2007). Jump Diffusion Processes and Their Applications in Insurance and Finance. Insurance, Mathematics and Economics, 41(1):62–70.CrossRefGoogle Scholar
Jankel, N. (2015). AI vs Human Intelligence: Why Computers Will Never Create Disruptive Innovations. Huffington Post, 26 April.Google Scholar
Jarmin, R. and O’Hara, A. (2016). Big Data and the Transformation of Public Policy Analysis. Journal of Policy Analysis and Management, 35(3):715–721.CrossRefGoogle Scholar
Jenkins, P., Farag, A., Jenkins, J., Yao, H., Wang, S., and Li, Z. (2021). Neural Utility Functions. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 35(9):7917–7925.CrossRefGoogle Scholar
Johansen, A. and Sornette, D. (2001). Finite-time Singularity in the Dynamics of the World Population: Economic and Financial Indices. Physica A, 294(3–4):465–502.CrossRefGoogle Scholar
Jones, B. (2009). The Burden of Knowledge and the Death of Renaissance Man: Is Innovation Getting Harder? Review of Economic Studies, 76(1):283–317.CrossRefGoogle Scholar
Jones, C. (1995). R&D-based Models of Economic Growth. Journal of Political Economy, 103(4):759–783.CrossRefGoogle Scholar
Jones, C. (2001). Was An Industrial Revolution Inevitable? Economic Growth Over the Very Long Run. Advances in Macroeconomics, 1(2):article 1.Google Scholar
Jones, C. (2022). The End of Economic Growth? Unintended Consequences of a Declining Population. American Economic Review, 112(11):3489–3527.CrossRefGoogle Scholar
Jones, N. (2018). How to Stop Data Centres from Gobbling up the Worlds Electricity. Nature, 561:163–166.CrossRefGoogle ScholarPubMed
Jorgensen, S., Kort, P., and Dockner, E. (2006). Venture Capital Financed Investments in Intellectual Capital. Journal of Dynamics and Control, 30:2339–2361.CrossRefGoogle Scholar
Joshi, N. (2019). How Far Are We From Achieving Artificial General Intelligence? Forbes, 10 June.Google Scholar
Jumper, J., Evans, R., Pritzel, A., and et al. (2021). Highly Accurate Protein Structure Prediction with AlphaFold. Nature, 596(7873):583–589.CrossRefGoogle ScholarPubMed
Kahneman, D., Sibony, O., and Sunstein, C. (2021). Noise: A Flaw in Human Judgment. London: HarperCollins.Google Scholar
Kamatani, N. (2021). Genes, the Brain, and Artificial Intelligence in Evolution. Journal of Human Genetics, 66(1):103–109.CrossRefGoogle ScholarPubMed
Kanbach, D., Heiduk, K., Blueher, G., Schreiter, M., and Lahmann, A. (2023). The GenAI Is Out of the Bottle: Generative Artificial Intelligence from a Business Model Innovation Perspective. Review of Managerial Science, September.Google Scholar
Karabarbounis, L. and Neiman, B. (2014). The Global Decline of the Labor Share. Quarterly Journal of Economics, 129(1):61–103.CrossRefGoogle Scholar
Karnofsky, H. (2021a). Digital People Would Be An Even Bigger Deal. Cold Takes Blog, 27 July.Google Scholar
Karnofsky, H. (2021b). Forecasting Transformative AI, Part 1: What Kind of AI? Cold Takes Blog, 10 August.Google Scholar
Karnofsky, H. (2021c). This Can’t Go On. Effective Altruism Forum, 3 August.Google Scholar
Karnofsky, H. (2021d). Weak Point in a Most Important Century: Lock-In. Cold Takes Blog.Google Scholar
Kim, G., Trimi, S., and Chung, J. (2014). Big-data Applications in the Government Sector. Communications of the ACM, 57(3):78–85.CrossRefGoogle Scholar
Kirchner, J., Smith, L., and Thibodeau, J. (2022). Understanding AI Alignment Research: A Systematic Analysis. arXiv:2206.02841v1 [cs.CY].Google Scholar
Kitchin, R. and McArdle, G. (2016). What Makes Big Data, Big Data? Exploring the Ontological Characteristics of 26 Datasets. Big Data and Society, 3(1):1–10.CrossRefGoogle Scholar
Knight, F. (1933). Risk, Uncertainty and Profit. Boston: Houghton Mifflin Co.Google Scholar
Knox, J. (2022). The Metaverse, or the Serious Business of Tech Frontiers. Postdigital Science Education, 4(1):207–215.CrossRefGoogle Scholar
Koch, C. (2012). Modular Biological Complexity. Science, 337(6094):531–532.CrossRefGoogle ScholarPubMed
Kogan, S., Levin, D., Routledge, B. R., Sagi, J., and Smith, N. (2009). Predicting Risk from Financial Reports with Regression. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 272–280.CrossRefGoogle Scholar
Kokotajlo, D. and Dai, W. (2019). The Main Sources of AI Risk? AI Alignment Forum, 21 March.Google Scholar
Konrad, K. (2009). Strategy and Dynamics in Contests. Oxford: Oxford University Press.CrossRefGoogle Scholar
Koppl, R., Deveraux, A., Herriot, J., and Kauffman, S. (2019). The Industrial Revolution as a Combinatorial Explosion. Mimeo. Syracuse: Syracuse University.Google Scholar
Korinek, A. (2019). Integrating Ethical Values and Economic Value to Steer Progress in Artificial Intelligence. NBER Working Paper no. 26130, National Bureau of Economic Research.CrossRefGoogle Scholar
Korinek, A. (2023). Generative AI for Economic Research: Use Cases and Implications for Economists. Mimeo, Forthcoming in Journal of Economic Literature, 61(4):1281–1317.CrossRefGoogle Scholar
Korinek, A. and Stiglitz, J. (2017). Artificial Intelligence and Its Implications for Income Distribution and Unemployment. NBER Working Paper no. 24174. National Bureau for Economic Research.CrossRefGoogle Scholar
Korinek, A. and Stiglitz, J. E. (2021). Artificial Intelligence, Globalization, and Strategies for Economic Development. Institute for New Economic Thinking Working Paper Series No. 146.CrossRefGoogle Scholar
Kotlikoff, L. (2022). Does Prediction Machines Predict Our AI Future? A Review. Journal of Economic Literature, 60(3):1052–1057.CrossRefGoogle Scholar
Kou, S. (2002). A Jump-Diffusion Model for Option Pricing. Management Science, 48(8):1086–1101.CrossRefGoogle Scholar
Kou, S. and Wang, H. (2003). First Passage Time of a Jump Diffusion Process. Advances in Applied Probability, 35(2):504–531.CrossRefGoogle Scholar
Kremer, M. (1993). The O-Ring Theory of Economic Development. The Quarterly Journal of Economics, 108(3):551–575.CrossRefGoogle Scholar
Krugman, P. (1978). The Theory of Interstellar Trade. Mimeo: Yale University.Google Scholar
Kumar, M., Mani, U., Tripathi, P., Saalim, M., and Roy, S. (2023). Artificial Hallucinations by Google Bard: Think Before You Leap. Cureus, 15(8):e43313.Google ScholarPubMed
Kuriksha, A. (2021). An Economy of Neural Networks: Learning from Heterogeneous Experiences. PIER Working Paper 21-027, University of Pennsylvania.CrossRefGoogle Scholar
Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. New York: Viking Press.Google Scholar
Kuthunur, S. (2023). New AI Algorithm Can Detect Signs of Life with 90% Accuracy. Scientists Want to Send It to Mars. Space.com., 27 September.Google Scholar
Kutyniok, G. (2022). The Mathematics of Artificial Intelligence. arXiv preprint arXiv:2203.08890.Google Scholar
Kydd, A. (2015). International Relations Theory: The Game-theoretic Approach. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Lambert, N., Castricato, L., von Werra, L., and Havrilla, A. (2022). Illustrating Reinforcement Learning from Human Feedback (RLHF). https://huggingface.co/blog/rlhf, 9 December.Google Scholar
Lampton, M. (2013). Information-driven Societies and Fermi’s Paradox. International Journal of Astrobiology, 12(4):312–313.CrossRefGoogle Scholar
Landes, D. (1999). The Wealth and Poverty of Nations: Why Some Are So Rich and Some So Poor. New York: Norton & Co.Google Scholar
Lane, J. (2016). Big Data for Public Policy: The Quadruple Helix. Journal of Policy Analysis and Management, 35(3):708–715.CrossRefGoogle Scholar
Larnier, J. (2023). There Is No A.I. Annals of Artificial Intelligence, The New Yorker, 20 April.Google Scholar
Lavertu, S. (2014). We All Need Help: Big Data and the Mismeasure of Public Administration. Public Administration Review, 76(6):864–872.CrossRefGoogle Scholar
Lazer, D., Kennedy, R., King, G., and Vespignani, A. (2014). The Parable of Google Flu: Traps in Big Data Analysis. Science, 343(6176):1203–1205.CrossRefGoogle ScholarPubMed
LeCun, Y. (2022). A Path Towards Autonomous Machine Intelligence – version 0.9.2, 2022-06-27. Mimeo: Courant Institute of Mathematical Sciences, New York University.Google Scholar
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep Learning. Nature, 521(7553):436–444.CrossRefGoogle ScholarPubMed
Lee, C. (2019). The Game of Go: Bounded Rationality and Artificial Intelligence. Yusof Ishak Institute Working Paper no. 2019-04.Google Scholar
Lee, R. (1988). Induced Population Growth and Induced Technological Progress: Their Interaction in the Accelerating Stage. Mathematical Population Studies, 1(3):265–288.CrossRefGoogle ScholarPubMed
Leibfried, F. and Braun, D. A. (2016). Bounded Rational Decision-making in Feedforward Neural Networks. arXiv:1602.08332v2 [cs.AI].Google Scholar
Leike, J., Martic, M., Krakovna, V., Ortega, P., Everitt, T., Lefrancq, A., Orseau, L., and Legg, S. (2017). AI Safety Gridworlds. ArXiv:1711.09883 [Cs], November 27: http://arxiv.org/abs/1711.09883.Google Scholar
Lenderink, B., Johannes, I., and Voordijk, H. (2019). Innovation and Public Procurement: From Fragmentation to Synthesis on Concepts, Rationales and Approaches. Innovation: The European Journal of Social Science Research, 35(4):650–674. https://doi.org/10.1080/13511610.2019.1700101.Google Scholar
Lenharo, M. (2023a). Decades-long Bet on Consciousness Ends – and it’s Philosopher 1, Neuroscientist 0. Nature, 619(7968):14–15.CrossRefGoogle ScholarPubMed
Lenharo, M. (2023b). If AI Becomes Conscious: Heres How Researchers Will Know. Nature, 24 August.CrossRefGoogle Scholar
LeRoy, S. and Singell, L. (1987). Knight on Risk and Uncertainty. Journal of Political Economy, 95(2):394–406.CrossRefGoogle Scholar
Lichtner-Bajjaoui, A. (2020). A Mathematical Introduction to Neural Networks. Advanced Mathematics Master Thesis, Universitat de Barcelona.Google Scholar
Lin, J. (2020). Industrial Policy Revisited. In: WTO, World Trade Report 2020: Government Policies to Promote Innovation in the Digital Age. Geneva. World Trade Organization, p. 26.Google Scholar
Lipnowski, E. and Doron, R. (2022). Predicting Choice from Information Costs. arXiv:2205.10434.Google Scholar
List, J. and Haigh, M. (2005). A Simple Test of Expected Utility Theory using Professional Traders. Proceedings of the National Academy of Science USA, 102(3):945–948.CrossRefGoogle ScholarPubMed
Liu, C. (2008). The Dark Forest. New York: Tom Doherty Associates.Google Scholar
Liu, F., Shi, Y., and Li, P. (2017). Analysis of the Relation Between Articial Intelligence and the Internet from the Perspective of Brain Science. Procedia Computer Science, pp. 377–383.CrossRefGoogle Scholar
Lomborg, B. (2020). Welfare in the 21st Century: Increasing Development, Reducing Inequality, the Impact of Climate Change, and the Cost of Climate Policies. Technological Forecasting and Social Change, 156(3):119981.CrossRefGoogle Scholar
Ludvigsen, K. (2023). The Carbon Footprint of GPT-4. Medium: Towards Data Science, 18 July.Google Scholar
Maas, M. (2018). Regulating for Normal AI Accidents: Operational Lessons for the Responsible Governance of Artificial Intelligence Deployment. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 223–228.CrossRefGoogle Scholar
MacAskill, W. (2022). What We Owe The Future. New York: Basic Books.Google Scholar
Macaulay, T. (2021). Slaughterbots Are a Step Away from Your Neighborhood – And We Need a Ban. Neural, 13 December.Google Scholar
Marcus, G. (2015). Machines Won’t Be Thinking Anytime Soon. Edge.Google Scholar
Marcus, G. (2022). Artificial General Intelligence Is Not as Imminent as You Might Think. Scientific American, 1 July.Google Scholar
Margetts, H. and Sutcliffe, D. (2013). Addressing the Policy Challenges and Opportunities of Big Data. Policy and Internet, 5(2):139–146.CrossRefGoogle Scholar
Martin, I. and Pindyck, R. S. (2015). Averting Catastrophes: The Strange Economics of Scylla and Charybdis. American Economic Review, 105(10):2947–2985.CrossRefGoogle Scholar
Martins, N., Angelica, A., Chakravarthy, K., Svidinenko, Y., Boehm, F., Opris, I., Lebedev, M., Swan, M., Garan, S., Rosenfeld, J., Hogg, T., and Freitas, R. (2019). Human Brain/Cloud Interface. Frontiers of Neuroscience, 13:112.CrossRefGoogle ScholarPubMed
Marvel, M. and Lumpkin, G. (2007). Technology Entrepreneurs’ Human Capital and Its Effects on Innovation Radicalness. Entrepreneurship Theory and Practice, 31(6):807–828.CrossRefGoogle Scholar
Maschler, M., Solan, E., and Zamir, S. (2013). Game (t)heory, 2nd ed. Cambridge: Cambridge University Press.Google Scholar
Matloff, G. (2022). Von Neumann Probes: Rationale, Propulsion, Interstellar Transfer Timing. International Journal of Astrobiology, 21(4):205–211.CrossRefGoogle Scholar
Matthews, D. (2022). Humanity was Stagnant for Millennia – Then Something Big Changed 150 Years Ago. Vox, September 7.Google Scholar
Mazzucato, M. (2015). The Entrepreneurial State: Debunking Public vs. Private Sector Myths. London: Penguin Books.Google Scholar
Mazzucato, M. (2018). Mission-oriented Innovation Policies: Challenges and opportunities. Industrial and Corporate Change, 27(5):803–815.CrossRefGoogle Scholar
Mazzucato, M. (2020). Mission-oriented Innovation and Industrial Policies. In WTO, World Trade Report 2020: Government Policies to Promote Innovation in the Digital Age. Geneva: World Trade Organization, p. 101.Google Scholar
McAfee, A. and Brynjolfsson, E. (2017). Machine, Platform, Crowd: Harnessing our Digital Future. New York: W.W. Norton & Company.Google Scholar
McElheran, K., Li, J., Brynjolfsson, E., Kroff, Z., Dinlersoz, E., Foster, L., and Zolas, N. (2023). AI Adoption in America: Who, What, and Where. NBER Working Paper No. w31788.CrossRefGoogle Scholar
McMahon, B. (2022). AI is Ushering In a New Scientific Revolution. The Gradient, 4 June.Google Scholar
Meese, T. (2018). The How and Why of Consciousness? Frontiers in Psychology, 9:2173.CrossRefGoogle ScholarPubMed
Mergel, I., Rethemeyer, K., and Isett, K. (2016). Big Data in Public Affairs. Public Administration Review, 76(6):928–937.CrossRefGoogle Scholar
Merton, R. (1976). Option Pricing when the Stock Returns are Discontinuous. Journal of Financial Economics, 3(1–2):125–144.CrossRefGoogle Scholar
Michael, K. and Miller, K. (2013). Big Data: New Opportunities and New Challenges. Computer, 46(6):22–24.CrossRefGoogle Scholar
Miller, F. and Lehoux, P. (2020). The Innovation Impacts of Public Procurement Offices: The Case of Healthcare Procurement. Research Policy, 49(7):1–13.CrossRefGoogle Scholar
Miller, J. and Felton, D. (2017). The Fermi Paradox, Bayes’ Rule, and Existential Risk Management. Futures, 86:44–57.CrossRefGoogle Scholar
Minsky, M. (1961). Steps Towards Articial Intelligence. https://web.media.mit.edu/minsky/papers/steps.html.Google Scholar
Mokyr, J. (2002). The Gifts of Athena: Historical Origins of the Knowledge Economy. Princeton: Princeton University Press.Google Scholar
Mokyr, J. (2014). Growth and Technology: The Wild Ride Ahead. Milken Institute Review, April 28.Google Scholar
Mokyr, J. (2016). A Culture of Growth: The Origins of the Modern Economy. Princeton: Princeton University Press.CrossRefGoogle Scholar
Monton, B. (2019). How to Avoid Maximizing Expected Utility. Philosophers Imprint, 19(8):1–25.Google Scholar
Moor, J. (2006). The Dartmouth College Artificial Intelligence Conference: The Next Fifty Years. AI Magazine, 27(4):87–91.Google Scholar
Moore, G. (1965). Cramming More Components onto Integrated Circuits. Electronics, 38(8):33–35.Google Scholar
Moscati, I. (2016). Retrospectives: How Economists Came to Accept Expected Utility Theory: The Case of Samuelson and Savage. Journal of Economic Perspectives, 30(2):219–236.CrossRefGoogle Scholar
Mubayi, P., Cheng, E., Terry, H., Tilton, A., Hou, T., Lu, D., Keung, R., and Liu, F. (2017). Chinas Rise in Artificial Intelligence. Goldman Sachs: Equity Research.Google Scholar
Mullainathan, S. and Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2):87–106.CrossRefGoogle Scholar
Müller, V. (2014). Risks ofGeneral Artificial Intelligence. Journal of Experimental and Theoretical Artificial Intelligence, 26(3):297–301.CrossRefGoogle Scholar
Murphy, T. (2012). Exponential Economist Meets Finite Physicist. Do The Math Blog, 4 October.Google Scholar
Murphy, T. (2022a). Human Exceptionalism. Do the Math Blog, 16 February.Google Scholar
Murphy, T. (2022b). Limits to Economic Growth. Nature Physics, 18(8):844–847.CrossRefGoogle Scholar
Murray, A., Kuban, S., Josefy, M., and Anderson, J. (2021). Contracting in the Smart Era: The Implications of Blockchain and Decentralized Autonomous Organizations for Contracting and Corporate Governance. Academy of Management Perspectives, 35(4):622–641.CrossRefGoogle Scholar
Science, National and Council, Technology (2016). The National Artificial Intelligence Research and Development Strategic Plan. Executive Office of the President of the United States. October.Google Scholar
Naudé, W. (2019a). The Economic and Business Impacts of Artificial Intelligence: Reality, not Hype. Medium: Towards Data Science, 10 June.Google Scholar
Naudé, W. (2019b). The Race Against the Robots and the Fallacy of the Giant Cheesecake: Immediate and Imagined Impacts of Artificial Intelligence. IZA Discussion Paper no. 12218.CrossRefGoogle Scholar
Naudé, W. (2021). Artificial Intelligence: Neither Utopian Nor Apocalyptic Impacts Soon. Economics of Innovation and New Technology, 30(1):1–24.CrossRefGoogle Scholar
Naudé, W. (2022). From the Entrepreneurial to the Ossified Economy. Cambridge Journal of Economics, 46(1):105–131.CrossRefGoogle Scholar
Naudé, W. (2023a). Destructive Digital Entrepreneurship. IZA Discussion Paper No. 16483. Bonn: IZA Institute for Labour Economics.Google Scholar
Naudé, W. (2023b). Late Industrialisation and Global Value Chains under Platform Capitalism. Journal of Industrial and Business Economics, 50(3):91–119.CrossRefGoogle Scholar
Naudé, W. and Dimitri, N. (2018). The Race for an Artificial General Intelligence: Implications for Public Policy. IZA Discussion Paper no. 11737, IZA Institute of Labor Economics.Google Scholar
Naudé, W. and Dimitri, N. (2020). The Race for an Artificial General Intelligence: Implications for Public Policy. AI and Society, 35(2):367–379.CrossRefGoogle Scholar
Naudé, W. and Dimitri, N. (2021). Public Procurement and Innovation for Human-centered Artificial Intelligence. IZA Discussion Paper.CrossRefGoogle Scholar
Naudé, W. and Nagler, P. (2018). Technological Innovation, Entrepreneurship and Productivity in Germany, 1871-2015. SPRU Working Paper Series (SWPS), 2018-02. Science Policy Research Unit, University of Sussex.CrossRefGoogle Scholar
Naudé, W. and Vinuesa, R. (2021). Data Deprivations, Data Gaps and Digital Divides: Lessons from the COVID-19 Pandemic. Big Data and Society, 8(2):1–12.CrossRefGoogle Scholar
Navarria, G. (2016). How the Internet was Born: From the ARPANET to the Internet. The Conversation, 3 November.Google Scholar
Neck, R. (2021). Methodological Individualism: Still a Useful Methodology for the Social Sciences? Atlantic Economic Journal, 49(2):349–361.CrossRefGoogle Scholar
Newquist, H. (1994). The Brain Makers: Genius, Ego, and Greed in the Quest for Machines That Think. New York: Macmillan.Google Scholar
Nguyen, A., Yosinski, J., and Clune, J. (2014). Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. ArXiv, https://arxiv.org/abs/1412.1897.Google Scholar
Nordhaus, W. (2015). Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth. Cowles Foundation Discussion Paper no. 2021. Yale University.CrossRefGoogle Scholar
Nordhaus, W. (2021). Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth. American Economic Journal: Macroeconomics, 13(1):299–332.Google Scholar
Nordhaus, W. D. (2009). An Analysis of the Dismal Theorem. Cowles Foundation Discussion Paper no. 1686.Google Scholar
North, D. (1991). Institutions. Journal of Economic Perspectives, 5(1):97–112.CrossRefGoogle Scholar
Noy, I. and Uher, T. (2022). Four New Horsemen of an Apocalypse? Solar Flares, Super-Volcanoes, Pandemics, and Artificial Intelligence. Economics of Disasters and Climate Change, 6(2):393–416.CrossRefGoogle ScholarPubMed
O’Brien, J. and Nelson, C. (2020). Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology. Health Security, 187(3):219–227.Google Scholar
Obwegeser, N. and Müller, S. (2018). Innovation and Public Procurement: Terminology, Concepts, and Applications. Technovation, 74–75:1–17.CrossRefGoogle Scholar
O’Connell, M. (2017). To Be A Machine: Adventures Among Cyborgs, Utopians, Hackers, and the Futurists Solving the Modest Problem of Death. New York: Doubleday Books.Google Scholar
O’Connor, G. and McDermott, C. (2004). The Human Side of Radical Innovation. Journal of Engineering and Technology Management, 21(1–2):11–30.CrossRefGoogle Scholar
OECD (2017). Business Dynamics and Productivity.Google Scholar
OECD (2019). Recommendation of the Council on Artificial Intelligence. OECD/LEGAL/0449, Adopted 22 May 2019. Paris: Organization for Economic Cooperation and Development.Google Scholar
Oesterheld, C. (2021). Approval-directed Agency and the Decision Theory of Newcomb-like Problems. Synthese, 198(27):6491–6504.CrossRefGoogle Scholar
Olson, S. J. (2015). Homogeneous Cosmology with Aggressively Expanding Civilizations. Classical and Quantum Gravity, 32:215025.CrossRefGoogle Scholar
Omohundro, S. (2008a). The Basic AI Drives. Proceedings of the First AGI Conference.Google Scholar
Omohundro, S. (2008b). The Nature of Self-improving Artificial Intelligence. Mimeo Palo Alto: Self-aware Systems.Google Scholar
Ord, T. (2020). The Precipice: Existential Risk and the Future of Humanity. New York: Hachette Books.Google Scholar
Østergaard, A. and Nielbo, K. (2023). False Responses from Artificial Intelligence Models Are Not Hallucinations. Schizophrenia Bulletin, 49(5):1105–1107.CrossRefGoogle ScholarPubMed
Parkes, D. and Wellman, M. P. (2015). Economic Reasoning and Artificial Intelligence. Science, 6245:267–272.CrossRefGoogle Scholar
Parret, A. (2020). Neural Networks in Economics. PhD Dissertation, University of California, Irvine.Google Scholar
Parshley, L. (2023). Artificial Intelligence Could Finally Let Us Talk with Animals. Scientific American, 1 October.Google Scholar
Pastor-Berniera, A., Plott, C., and Schultz, W. (2017). Monkeys Choose as If Maximizing Utility Compatible with Basic Principles of Revealed Preference Theory. PNAS, 114(10):E1766–E1775.Google Scholar
Pattanayak, K. and Krishnamurthy, V. (2021). Rationally Inattentive Utility Maximization Explains Deep Image Classification. arXiv:2102.04594 [cs.LG].Google Scholar
Pearl, J. (1985). Bayesian Networks: a Model of Self-activated Memory for Evidential Reasoning. Proceedings of the 7th conference of the Cognitive Science Society, University of California, Irvine, CA, USA.Google Scholar
Persky, J. (1995). Retrospectives: The Ethology of Homo Economicus. Journal of Economic Perspectives, 9(2):221–231.CrossRefGoogle Scholar
Petigura, E. A., Howard, A., and Marcya, G. (2013). Prevalence of Earth-size Planets Orbiting Sun-like Stars. PNAS, 110(48):19273–19278.CrossRefGoogle ScholarPubMed
Pham, H. (1997). Optimal Stopping, Free Boundary, and American Option in a Jump-Diffusion Model. Applied Mathematics and Optimization, 35(2):145–164.CrossRefGoogle Scholar
Pigozzi, G., Tsoukias, A., and Viappiani, P. (2016). Preferences in Artificial Intelligence. Annals of Mathemathical Artificial Intelligence, 77(3–4):361–401.CrossRefGoogle Scholar
Pinker, S. (2018). Enlightenment Now: The Case for Reason, Science, Humanism, and Progress. New York: Viking.Google Scholar
Pissarides, C. (2018). Work in the Age of Robots and AI. IZA 20th Anniversary Celebration, Berlin, 28–29 June.Google Scholar
Pistono, F. and Yampolskiy, R. (2016). Unethical Research: How to Create a Malevolent Artificial Intelligence. arXiv:1605.02817 [cs.AI].Google Scholar
Pooley, G. and Tupy, M. L. (2018). The Simon Abundance Index: A New Way to Measure Availability of Resources. Policy Analysis no. 857, Cato Institute.Google Scholar
Prettner, K. and Strulik, H. (2017). The Lost Race Against the Machine: Automation, Education, and Inequality in an R&D-based Growth Model. Hohenheim Discussion Papers in Business, Economics and Social Sciences no. 08-2017.CrossRefGoogle Scholar
Pulkka, V. (2017). A Free Lunch with Robots – Can a Basic Income Stabilise the Digital Economy? Transfer, 23(3):295–311.CrossRefGoogle Scholar
Ramsey, F. (1931). Truth and Probability. In Ramsey, F. (ed.). Foundations of Mathematics and other Logical Essays. New York: Harcourt.Google Scholar
Rees, M. (2018). On The Future: Prospects for Humanity. Princeton: Princeton University Press.Google Scholar
Rees, M. (2021). Seti: Why Extraterrestrial Intelligence Is More Likely to be Artificial than Biological. The Conversation, 18 October.Google Scholar
Resing, W. (2005). Intelligence Testing. In Encyclopedia of Social Measurement, Kempf-Leonard, K. (ed.) Elsevier, pp. 307–315.CrossRefGoogle Scholar
Ricci, F., Rokach, L., and Shapira, B. (2022). Recommender Systems: Techniques, Applications, and Challenges. In: Ricci, F., Rokach, L., and Shapira, B. (eds.). Recommender Systems Handbook (3rd ed.). New York: Springer, pp. 1–35.CrossRefGoogle Scholar
Ridley, M. (2011). The Rational Optimist: How Prosperity Evolves. London: Fourth Estate.Google Scholar
Ridley, M. (2015). The Myth of Basic Science. The Wall Street Journal, 23 October.Google Scholar
Riedel, C. J. (2021). Value Lock-in Notes. Mimeo, 25 July.Google Scholar
Riedl, M. (2019). Human-centered Artificial Intelligence and Machine Learning. Human Behavior and Emerging Technologies, 1(1):33–36.CrossRefGoogle Scholar
Ring, M. and Orseau, L. (2011). Delusion, Survival, and Intelligent Agents. In: Schmidhuber, J., Thórisson, K. R., and Looks, M. (eds.) AGI 2011. LNCS (LNAI), 6830:11–20.Google Scholar
Rodriguez, F. and Jayadev, A. (2010). The Declining Labor Share of Income. Human Development Research Paper No. 2010/36, UNDP.Google Scholar
Romer, P. (1986). Increasing Returns and Long-run Growth. Journal of Political Economy, 94(5):1002–1037.CrossRefGoogle Scholar
Romer, P. (1987). Growth Based on Increasing Returns Due to Specialization. American Economic Review, 77(2):56–62.Google Scholar
Romer, P. (1990). Endogenous Technical Change. World Development, 98(5):S71–S102.Google Scholar
Romer, P. (2019). The Deep Structure of Economic Growth. Blog. https://paulromer.net, 5 February.Google Scholar
Roodman, D. (2020). Modelling the Human Trajectory. Open Philanthropy, 15 June.Google Scholar
Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, 65(6):386–407.CrossRefGoogle ScholarPubMed
Roser, M., Ritchie, H., and Mathieu, E. (2023). What is Moore’s Law? Our World in Data, 28 March.Google Scholar
Rumelhart, D., Hinton, G., and Williams, R. (1986). Learning Representations by Back-Propagating Errors. Nature, 323(6088):533–536.CrossRefGoogle Scholar
Russel, S., Dewey, D., and Tegmark, M. (2015). Research Priorities for Robust and Beneficial Artificial Intelligence. AI Magazine, Association for the Advancement of Artificial Intelligence, Winter, pp. 105–114.CrossRefGoogle Scholar
Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. New York: Viking Books.Google Scholar
Russell, S. and Norvig, P. (2021). Artificial Intelligence: A Modern Approach. 4th ed. London: Pearson Education, Inc.Google Scholar
Sachs, J., Benzell, S., and LaGardia, G. (2015). Robots: Curse or Blessing? A Basic Framework. Working Paper 21091. National Bureau of Economic Research.CrossRefGoogle Scholar
Salimans, T., J., Chen, X., Sidor, S., and Sutskever, I. (2017). Evolution Strategies as a Scalable Alternative to Reinforcement Learning. arXiv:1703.03864v2 [stat.ML].Google Scholar
Samuel, A. L. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3(3):210–229.CrossRefGoogle Scholar
Samuel, S. (2022). Effective Altruism’s Most Controversial Idea. Vox, 6 September.Google Scholar
Samuelson, P. (1947). Foundations of Economic Analysis. Cambridge: Harvard University Press.Google Scholar
Sandberg, A. (2013). An Overview of Models of Technological Singularity. In: More, M. and Vita-More, N. (eds.). The Transhumanist Reader. New York: Wiley.Google Scholar
Sandberg, A. (2014). Ethics of Brain Emulations. Journal of Experimental and Theoretical Artificial Intelligence, 26(3):439–457.CrossRefGoogle Scholar
Sandberg, A. (2018). Space Races: Settling the Universe Fast. Mimeo: Future of Humanity Institute, Oxford Martin School, University of Oxford.Google Scholar
Sandberg, A. and Bostrom, N. (2008). Whole Brain Emulation: A Roadmap. Technical Report 2008–3, Future of Humanity Institute, Oxford University.Google Scholar
Santana, C. and Albareda, L. (2022). Blockchain and the Emergence of Decentralized Autonomous Organizations (DAOs): An Integrative Model and Research Agenda. Technological Forecasting and Social Change, 182:121806.CrossRefGoogle Scholar
Sarkar, S. (2000). On the Investment-Uncertainty Relationship in a Real Options Model. Journal of Economic Dynamics and Control, 24(2):219–225.CrossRefGoogle Scholar
Sarker, I. (2021). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science, 2(6):420.CrossRefGoogle ScholarPubMed
Savage, L. (1954). The Foundations of Statistics. New York: Dover.Google Scholar
Schiller, R. (2019). Narratives about Technology-induced Job Degradations Then and Now. Working Paper no. 25536. National Bureau of Economic Research.CrossRefGoogle Scholar
Schintler, L. and Kulkarni, R. (2014). Big Data for Policy Analysis: The Good, The Bad, and the Ugly. Review of Policy Research, 31(4):343–348.CrossRefGoogle Scholar
Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks, 61:85–117.CrossRefGoogle ScholarPubMed
Schoemaker, P. (1982). The Expected Utility Model: Its Variants, Purposes, Evidence and Limitations. Journal of Economic Literature, 20(2):529–563.Google Scholar
Schreiner, M. (2023). GPT-4 Architecture, Datasets, Costs and More Leaked. The Decoder, 11 July.Google Scholar
Schwab, K. (2016). The Fourth Industrial Revolution: What It Means, How to Respond. Davos: World Economic Forum.Google Scholar
Schwartz, R., Dodge, J., Smith, N., and Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12):54–63.CrossRefGoogle Scholar
Searle, J. (1980). Minds, Brains and Programs. Behavioral and Brain Sciences, 3(3):417–457.CrossRefGoogle Scholar
Searle, J. (1992). The Rediscovery of the Mind. Cambridge: MIT Press.CrossRefGoogle Scholar
Sejnowski, T. J. (2020). The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence. PNAS, 117(48):30033–30038.CrossRefGoogle ScholarPubMed
Sena, V. and Nocker, M. (2021). AI and Business Models: The Good, the Bad and the Ugly. Foundations and Trends in Technology, Information and Operations Management, 14(4):324–397.CrossRefGoogle Scholar
Setiya, K. (2022). The New Moral Mathematics. Boston Review, 15 August.Google Scholar
Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., and Villalobos, P. (2022). Compute Trends Across Three Eras of Machine Learning. arXiv:2202.05924 [cs.LG].Google Scholar
Shah, R. (2019a). AI Safety without Goal Directed Behaviour. Alignment Forum, 7 January.Google Scholar
Shah, R. (2019b). Stuart Russell’s New Book on Why We Need to Replace the Standard Model of AI. Alignment Newsletter no. 19.Google Scholar
Shannon, C. (1948). A Mathematical Theory of Communication. The Bell System Technical Journal, 27(3):379–423.CrossRefGoogle Scholar
Shneiderman, B. (2020). Human-centered Artificial Intelligence: Reliable, Safe & Trustworthy. International Journal of Human-Computer Interaction, 36(6):495–504.CrossRefGoogle Scholar
Shostak, S. (2018). Introduction: The True Nature of Aliens. International Journal of Astrobiology, 17(4):281.CrossRefGoogle Scholar
Shostak, S. (2021). If We Ever Encounter Aliens, They Will Resemble AI and Not Little Green Martians. The Guardian, 14 June.Google Scholar
Shulman, C. (2010). Omohundro’s “Basic AI Drives” and Catastrophic Risks. The Singularity Institute, San Francisco, CA.Google Scholar
Shulman, C. and Bostrom, N. (2021). Sharing the World with Digital Minds. In: Clarke, S., Zohny, H., and Savulescu, J. (eds.) Rethinking Moral Status. Oxford: Oxford Academic.Google Scholar
Siegel, R. (2009). All-Pay Contests. Econometrica, 77(1):71–92.Google Scholar
Simon, H. (1955). A Behavioral Model of Rational Choice. Quarterly Journal of Economics, 69(1):99–118.CrossRefGoogle Scholar
Simon, H. (1956). Rational Choice and the Structure of the Environment. Psychological Review, 63(2):129–138.CrossRefGoogle ScholarPubMed
Simon, H. (1978). On How to Decide What to Do. The Bell Journal of Economics, 9(2):494–507.CrossRefGoogle Scholar
Simon, H. (1996). The Sciences of the Artificial. Cambridge: MIT Press.Google Scholar
Simonite, T. (2020). The US Government Will Pay Doctors to Use These AI Algorithms. Wired Magazine, 11 October.Google Scholar
Sims, C. (2003). Implications of Rational Inattention. Journal of Monetary Economics, 50(3):665–690.CrossRefGoogle Scholar
Singer, P. (2021). The Hinge of History. Project Syndicate, 8 October.Google Scholar
Sivarajah, U., Kamal, M., and Weerakkody, V. (2017). Critical Analysis of Big Data Challenges and Analytical Methods. Journal of Business Research, 70(C):263–286.CrossRefGoogle Scholar
Smith, M. L. and Neupane, S. (2018). Artificial Intelligence and Human Development: Towards a Research Agenda. White Paper: International Development Research Centre.Google Scholar
Soice, E., Rocha, R., Cordova, K., Specter, M., and Esvelt, K. (2023). Can Large Language Models Democratize Access to Dual-use Biotechnology? arXiv preprint arXiv:2306.03809.Google Scholar
Sotala, K. (2018). Disjunctive Scenarios of Catastrophic AI Risk. In Yampolskiy, R. V. (ed.). Artificial Intelligence Safety and Security, 1st ed. Chapman and Hall/CRC.Google Scholar
Souza, K. D. and Jacob, B. (2017). Big Data in the Public Sector: Lessons for Practitioners and Scholars. Administration and Society, 49(7):1043–1064.Google Scholar
Srnicek, N. (2016). Platform Capitalism. London: Polity.Google Scholar
Stojčić, N., Srhoj, S., and Coad, A. (2020). Innovation Procurement as Capability-Building: Evaluating Innovation Policies in Eight Central and Eastern European Countries. European Economic Review, 121:1–18.CrossRefGoogle Scholar
Stokel-Walker, C. and Van Noorden, R. (2023). The Promise and Peril of Generative AI. Nature, 614(9):214–216.Google Scholar
Stolk, M. (2019). What To Do When Meeting ET? Masters Thesis in Political Science, Radboud University Nijmegen.Google Scholar
Stough, R. and McBride, D. (2014). Big Data and the U.S. Public Policy. Review of Policy Research, 31:339–342.CrossRefGoogle Scholar
Su, H. (2021). Game Theory and the Three-Body Problem. World Journal of Social Science Research, 8(1):17–33.CrossRefGoogle Scholar
Suleyman, M. (2023). The Coming Wave: Technology, Power, and the Twenty-first Century’s Greatest Dilemma. New York: Crown Books.Google Scholar
Susskind, D. (2017). A Model of Technological Unemployment. Economic Series Working Papers 819. University of Oxford.Google Scholar
Sutton, R. and Barto, A. (1998). Introduction to Reinforcement Learning, 1st ed. Cambridge: MIT Press.CrossRefGoogle Scholar
Tamura, H. (2009). Modeling Ambiguity Averse Behavior of Individual Decision Making: Prospect Theory under Uncertainty. In: Torra, V., Narukawa, Y., and Inuiguchi, M. (eds.). Modeling Decisions for Artificial Intelligence. MDAI 2009. Lecture Notes in Computer Science, vol. 5861. Berlin, Heidelberg: Springer.Google Scholar
Tarsney, C. (2020). The Epistemic Challenge to Longtermism. Mimeo: GPI, Oxford.Google Scholar
Taylor, I. (2016). Dependency Redux: Why Africa Is Not Rising. Review of African Political Economy, 43(147):8–25.CrossRefGoogle Scholar
Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. London: Penguin Books.Google Scholar
Thaler, R. (2000). From Homo Economicus to Homo Sapiens. Journal of Economic Perspectives, 14(1):133–141.CrossRefGoogle Scholar
Tien, J. (2017). Internet of Things, Real-time Decision Making, and Articial Intelligence. Annals of Data Science, 4(2):149–178.CrossRefGoogle Scholar
Tirole, J. (2021). Digital Dystopia. American Economic Review, 111(6):2007–2048.CrossRefGoogle Scholar
Tolan, S., Pesole, A., Martínez-Plumed, F., Fernández-Macías, E., Hernández-Orallo, J., and Gómez, E. (2020). Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks. JRC Working Papers Series on Labour, Education and Technology 2020/02, European Commission.Google Scholar
Torres, E. (2021). The Dangerous Ideas of “Longtermism” and “Existential Risk.” Current Affairs, 28 July.Google Scholar
Torres, E. (2022). Understanding “Longtermism”: Why This Suddenly Influential Philosophy Is So Toxic. Salon, 20 August.Google Scholar
Torres, P. (2014). Why Running Simulations May Mean the End is Near. Mimeo. https://archive.ieet.org/articles/torres20141103.html.Google Scholar
Totschnig, W. (2020). Fully Autonomous AI. Science and Engineering Ethics, 26(5):2473–2485.CrossRefGoogle ScholarPubMed
Trajtenberg, M. (2018). AI as the Next GPT: A Political-Economy Perspective. NBER Working Paper no. 24245. National Bureau for Economic Research.CrossRefGoogle Scholar
Tullock, G. (1980). Efficient Rent Seeking. In: Toward a Theory of the Rent Seeking Society, Buchanan, J. M., Tollison, R. D., and Tullock, G., (eds.) College Station: Texas A&M University Press, pp. 97–112.Google Scholar
Tunyasuvunakool, K., Adler, J., Wu, Z., and et al. (2021). Highly Accurate Protein Structure Prediction for the Human Proteome. Nature, 595(7873):590–596.CrossRefGoogle Scholar
Turchin, A. (2021). Catching Treacherous Turn: A Model of the Multilevel AI Boxing. Mimeo: Foundation Science for Life Extension, May.Google Scholar
Turchin, A. and Chernyakov, M. (2018). Classification of Approaches to Technological Resurrection. Mimeo, PhilArchive.Google Scholar
Turchin, A. and Denkenberger, D. (2020). Classification of Global Catastrophic Risks Connected with Artificial Intelligence. AI & Society, 35(1):147–163.CrossRefGoogle Scholar
Turing, A. (1936). On Computable Numbers, with an Application to the Entscheidungsproblem. London Mathematical Society, 42(1):230–265.Google Scholar
Turing, A. (1950). Computing Machinery and Intelligence. Mind, 59(236):433–460.CrossRefGoogle Scholar
Tzachor, A., Whittlestone, J., Sundaram, L., and ÓhÉigeartaigh, S. (2020). Artificial Intelligence in a Crisis Needs Ethics with Urgency. Nature Machine Intelligence, 2(3):365–366.CrossRefGoogle Scholar
Umbrello, S., Torres, P., and Bellis, De, A. (2020). The Future of War: Could Lethal Autonomous Weapons Make Conflict More Ethical? AI & Society, 35(1):273–282.CrossRefGoogle Scholar
UNESCO (2022). Recommendation on the Ethics of Artificial Intelligence. Paris: United Nations Educational, Scientific and Cultural Organization.Google Scholar
Urbina, F., Lentzos, F., Invernizzi, C., and Ekins, S. (2022). Dual Use of Artificial-Intelligence-powered Drug Discovery. Nature Machine Intelligence, 4(3):189–191.CrossRefGoogle ScholarPubMed
Uyarra, E., Flanagan, K., Magro, E., and Zabala-Iturriagagoitia, J. (2017). Anchoring the Innovation Impacts of Public Procurement to Place: The Role of Conversations. Environmental Planning, 35(5):828–848.Google Scholar
Valdes, F. and Jr, Freitas, A., R. (1980). Comparison of Reproducing and Nonreproducing Starprobe Strategies for Galactic Exploration. Journal of the British Interplanetary Society, 33:402–408.Google Scholar
Alstyne, Van, M., Parker, G., and Choudary, S. (2016). Pipelines, Platforms, and the New Rules of Strategy. Harvard Business Review, April:54–62.Google Scholar
Van de Gevel, A. and Noussair, C. (2013a). The Nexus between Artificial Intelligence and Economics. Berlin, Heidelberg: Springer Briefs in Economics.CrossRefGoogle Scholar
Van de Gevel, A. and Noussair, C. (2013b). The Nexus between Artificial Intelligence and Economics. Berlin, Heidelberg: Springer Briefs in Economics.CrossRefGoogle Scholar
van de Poel, I. (2020). Embedding Values in Artificial Intelligence (AI) Systems. Minds and Machines, September. https://doi.org/10.1007/s11023–020–09537–4.CrossRefGoogle Scholar
Zanden, Van, J., Baten, J., Foldvari, P., and van Leeuwen, B. (2014). The Changing Shape of Global Inequality, 1820-2000: Exploring a New Dataset. Review of Income and Wealth, 60(2):279–297.CrossRefGoogle Scholar
Varella, M. (2018). The Biology and Evolution of the Three Psychological Tendencies to Anthropomorphize Biology and Evolution. Frontier in Psychology, 1(9):1839.CrossRefGoogle Scholar
Varian, H. (1995). Economic Mechanism Design for Computerized Agents. Proceedings of the First USENIX Workshop on Electronic Commerce New York, New York, July.Google Scholar
Varian, H. (2014). Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2):3–28.CrossRefGoogle Scholar
Vaswani, A., Shazeer, N., Parmar, N., Uskoreit, J., Jones, L., Gomez, A., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. ArXiv.1706.03762.Google Scholar
Verendel, V. and Häggström, O. (2017). Fermi’s Paradox, Extraterrestrial Life and the Future of Humanity: A Bayesian Analysis. International Journal of Astrobiology, 16(1):14–18.CrossRefGoogle Scholar
Villaescusa-Navarro, F., Ding, J., Genel, S., Tonnesen, S., La Torre, V., Spergel, D., R. Teyssier, Li, Y., Heneka, C., Lemos, P., Anglés-Alcázar, D., Nagai, D., and Vogelsberger, M. (2022). Cosmology with One Galaxy? arXiv:2201.02202 [astro-ph.CO].Google Scholar
Vinge, V. (1993). The Coming Technological Singularity: How to Survive in the Post-human Era. VISION-21 Symposium, NASA Lewis Research Center and the Ohio Aerospace Institute, March 30–31.Google Scholar
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S., Tegmark, M., and Nerini, F. F. (2020). The Role of Artificial Intelligence in Achieving the Sustainable Development Goals. Nature Communications, 11(233).CrossRefGoogle ScholarPubMed
Vipra, J. and West, S. (2023). Computational Power and AI. AI Now Institute.Google Scholar
Vojnovic, M. (2015). Contest Theory. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Foerster, Von, H., Mora, P. M., and Amio, L. (1960). Doomsday: Friday, 13 November, A.D. 2026. Science, 132(3436):1291–1295.CrossRefGoogle Scholar
von Hippel, E. (1976). The Dominant Role of Users in the Scientific Instrument Innovation Process. Research Policy, 5(3):212–239.CrossRefGoogle Scholar
Neumann, Von, J. (1966). Theory of Self-reproducing Automata. Urbana and London: University of Illinois Press.Google Scholar
Von Neumann, J. and Morgenstern, O. (1944). Theory of Games and Economic Behavior, 1st ed. Princeton: Princeton University Press.Google Scholar
Wagner, D. (2020). Economic Patterns in a World with Artificial Intelligence. Evolutionary and Institutional Economics Review, 17(1):111–131.CrossRefGoogle Scholar
Waldrop, M. (2016). The Chips are Down for Moore’s Law. Nature, 530(7589):144–147.CrossRefGoogle ScholarPubMed
Wang, G., Gunasekaran, A., Ngai, E., and Papadopoulos, T. (2016). Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications. International Journal of Production Economics, 176:98–110.CrossRefGoogle Scholar
Ward, P. (2009). The Medea Hypothesis: Is Life on Earth Ultimately Self-destructive? Princeton: Princeton University Press.CrossRefGoogle Scholar
Warfield, D. (2023). Transformers – Intuitively and Exhaustively Explained. Medium Towards Data Science, 21 September.Google Scholar
Warwick, K. and Shah, H. (2016). Can Machines Think? A Report on Turing Test Experiments at the Royal Society. Journal of Experimental and Theoretical Artificial Intelligence, 2807(6):989–1007.CrossRefGoogle Scholar
Washington, A. (2014). Government Information Policy in the Era of Big Data. Review of Policy Research, 31(4):319–325.CrossRefGoogle Scholar
Webb, S. (2015). If the Universe is Teeming with Aliens … Where Is Every-body? Seventy-Five Solutions to the Fermi Paradox and the Problem of Extraterrestrial Life. Cham: Switzerland: Springer.Google Scholar
WEF (2018). Artificial Intelligence Collides with Patent Law. Center for the Fourth Industrial Revolution. Geneva: World Economic Forum.Google Scholar
Weiser, M. (1991). The Computer of the 21st Century. Scientific American, pp. 66–75.Google Scholar
Weitzman, M. (1998). Recombinant Growth. Quarterly Journal of Economics, 113(2):331–360.CrossRefGoogle Scholar
Weitzman, M. (2009). On Modeling and Interpreting the Economics of Catastrophic Climate Change. Review of Economics and Statistics, 91(1):1–19.CrossRefGoogle Scholar
Weizenbaum, J. (1966). ELIZA – A Computer Program for the Study of Natural Language Communication Between Man and Machine. Communications of the ACM, 9(1):36–45.CrossRefGoogle Scholar
Westby, T. and Conselice, C. (2020). The Astrobiological Copernican Weak and Strong Limits for Intelligent Life. The Astrophysical Journal, 896(58):1–18.CrossRefGoogle Scholar
Whalley, A. (2011). Optimal R&D Investment for a Risk-averse Entrepreneur. Journal of Economic Dynamics and Control, 35(4):413–429.CrossRefGoogle Scholar
Wheeler, J. (1989). Information, Physics, Quantum: The Search for Links. Reproduced from Proceedings of the 3rd International Symposium on the Foundations of Quantum Mechanics, Tokyo, pp. 354–368.Google Scholar
Wiley, K. (2011). The Fermi Paradox, Self-replicating Probes, and the Interstellar Transportation Bandwidth. arXiv:1111.6131v1 [physics.pop-ph].Google Scholar
Williams, G. (1966). Adaptation and Natural Selection: A Critique of Some Current Evolutionary Thought. Princeton: Princeton University Press.Google Scholar
WIPO (2019). Technology Trends 2019: Artificial Intelligence. Geneva: World Intellectual Property Association.Google Scholar
Wong, M. (2023). AI Doomerism is a Decoy. The Atlantic, 2 June.Google Scholar
Wooldridge, M. (2022). What Is Missing from Contemporary AI? The World. AAAS Intelligent Computing, 2022:9847630.Google Scholar
World Bank (2016). World Development Report 2016.Google Scholar
Wright, R. (2022). The Case for Shorttermism. Nonzero Blog, 11 August.Google Scholar
WTO (2020a). E-commerce, Trade and the COVID-19 Pandemic. Information Note. Geneva: World Trade Organization, 4 May.Google Scholar
WTO (2020b). World Trade Report 2020: Government Policies to Promote Innovation in the Digital Age. Geneva: World Trade Organization.Google Scholar
Yampolskiy, R. (2014). Utility Function Security in Artificially Intelligent Agents. Journal of Experimental and Theoretical Artificial Intelligence, 26(3):373–389.CrossRefGoogle Scholar
Yampolskiy, R. (2016). Taxonomy of Pathways to Dangerous Artificial Intelligence. The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence AI, Ethics, and Society: Technical Report WS-16-02.Google Scholar
Yampolskiy, R. V. (2012). Leakproofing the Singularity. Journal of Consciousness Studies, 19(1–2):194–214.Google Scholar
Yang, H. and Zhang, L. (2005). Optimal Investment for Insurers with Jump-Diffusion Risk Process. Insurance: Mathematics and Economics, 37(3):615–634.Google Scholar
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., and Yang, M.-H. (2022). Diffusion Models: A Comprehensive Survey of Methods and Applications. arXiv:2209.00796v10 [cs.LG].Google Scholar
Yasser, S. (2020). Aliens, The Fermi Paradox, And The Dark Forest Theory: A Game Theoretic View. Medium: Towards Data Science, 21 October.Google Scholar
Yilmaz, F. and Arabaci, O. (2020). Should Deep Learning Models be in High Demand, or Should they Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting. Computational Economics, 57(1):217–245.CrossRefGoogle Scholar
Yiu, C. (2012). The Big Data Opportunity: Making Government Faster, Smarter and More Personal. Policy Exchange.Google Scholar
Yoon, J. (2020). Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approaches. Computational Economics, 57(1):247–265.CrossRefGoogle Scholar
Yu, C. (2015). The Dark Forest Rule: One Solution to the Fermi Paradox. Journal of the British Interplanetary Society, 68(5–6):142–144.Google Scholar
Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V., and Yang, Q. (2018). Building Ethics into Artificial Intelligence. ArXiv, 7 December.CrossRefGoogle Scholar
Yudkowsky, E. (2001). Creating Friendly AI 1.0: The Analysis and Design of Benevolent Goal Architectures. The Singularity Institute, San Francisco, CA, June 15.Google Scholar
Yudkowsky, E. (2002). The AI-Box Experiment. www.yudkowsky.net/singularity/aibox.Google Scholar
Yudkowsky, E. (2007a). Levels of Organization in General Intelligence. In: Goertzel, B. and Pennachin, C. (eds.) Artificial General Intelligence Cognitive Technologies. Berlin: Springer, pp. 389–501.CrossRefGoogle Scholar
Yudkowsky, E. (2007b). Pascal’s Mugging: Tiny Probabilities of Vast Utilities. Less Wrong Blog, 19 October.Google Scholar
Yudkowsky, E. (2008). Artificial Intelligence as a Positive and Negative Factor in Global Risk. In: Bostrom, N. and Cirkovic, M. N. (eds.) Global Catastrophic Risks. Oxford: Oxford University Press, pp. 308–345.Google Scholar
Yudkowsky, E. (2009). Value is Fragile. Less Wrong Blog, 29 January.Google Scholar
Yudkowsky, E. (2017). There’s No Fire Alarm for Artificial General Intelligence. Machine Intelligence Research Institute, October 13.Google Scholar
Yudkowsky, E. (2023). Pausing AI Developments Isn’t Enough. We Need to Shut it All Down. Time Magazine, 23 March.Google Scholar
Zackrisson, E., Calissendorff, P., González, J., Benson, A., Johansen, A., and Janson, M. (2016). Terrestrial Planets Across Space and Time. The Astrophysical Journal, 833(2):1–12.CrossRefGoogle Scholar
Zhang, K., Yang, Z., and Basar, T. (2021). Multi-agent Reinforcement Learning: a Selective Overview of Theories and Algorithms. arXiv:1911.10635v2 [cs.LG].Google Scholar
Ziegler, D., Stiennon, N., Wu, J., Brown, T., Radford, A., Amodei, D., Christiano, P., and Irving, G. (2019). Fine-tuning Language Models from Human Preferences. arXiv preprint arXiv:1909.08593.Google Scholar
Zolas, N., Kroff, Z., Brynjolfsson, E., McElheran, K., Beede, D., Buffington, C., Goldschlag, N., Foster, L., and Dinlersoz, E. (2020). Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey. NBER Working Paper No. 28290.Google Scholar
Zuboff, S. (2015). Big Other: Surveillance Capitalism and the Prospects of an Information Civilization. Journal of Information Technology, 30(1):75–89.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Bibliography
  • Wim Naudé, Rheinisch-Westfälische Technische Hochschule, Aachen, Germany, Thomas Gries, Universität Paderborn, Germany, Nicola Dimitri, Università degli Studi, Siena
  • Book: Artificial Intelligence
  • Online publication: 23 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009483094.012
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Bibliography
  • Wim Naudé, Rheinisch-Westfälische Technische Hochschule, Aachen, Germany, Thomas Gries, Universität Paderborn, Germany, Nicola Dimitri, Università degli Studi, Siena
  • Book: Artificial Intelligence
  • Online publication: 23 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009483094.012
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Bibliography
  • Wim Naudé, Rheinisch-Westfälische Technische Hochschule, Aachen, Germany, Thomas Gries, Universität Paderborn, Germany, Nicola Dimitri, Università degli Studi, Siena
  • Book: Artificial Intelligence
  • Online publication: 23 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009483094.012
Available formats
×