With the emergence of big data and artificial intelligence, the feasibility of central planning has again become a popular topic. The essential feature of the planned economy is the use of systematic and institutional force to negate entrepreneurship and deprive individuals of the freedom to choose, especially the freedom to start a business and innovate. Can big data revive the planned economy? The essence of this question is: Can big data displace entrepreneurship? The impossibility of a big data-based planned economy is demonstrated from five perspectives (i.e. the nature of knowledge, the nature of entrepreneurial decisions, the distinction between risk and uncertainty, the importance of ideas, and the evolutionary view). In other words, big data cannot replace entrepreneurship. The false belief that central planning is possible with big data is extremely naïve.

When the Communist Party of China announced a new government on October 1, 1949, the economy that government inherited was in shambles. China had been at war for over twelve years and much of the infrastructure of the country had been destroyed or badly damaged and prices were rising at 51 percent per month or 13,000 percent per year. The Guomindang government fleeing to Taiwan took much of the country’s foreign-exchange and gold reserves with them, along with many of the managers of the banks and industrial firms. Inflation and war left many of the businesses that stayed barely able to function even when their managers and technicians did not flee.

Chapter 10 argues that the central contribution of Tinbergen is his decision models, which conceive of economic policy as the relation between instruments and goals. The chapter analyzes the way in which the original econometric models of the 1930s were transformed by Tinbergen (and Ragnar Frisch) into models for policymaking, in response to their new state positions. Tinbergen was director at the Central Planning Bureau in the Netherlands, which developed into the premier economic policy institute there. The transformation of econometric models into decision models no longer treated policy as a given, but instead treated behavioral economic relations as given, and the policy variables as decision variables, placing the economic expert inside the model. The chapter explores this fundamental transformation, and how it impacted Tinbergen’s own view of economics. He no longer believed that the primary goal was to describe the economic structure, but rather to design of (optimal) decision models to pursue targets. This was true at the firm and state levels. He sought to redefine crucial concepts such as unemployment and business cycles in policy terms. Finally, he used that transformation to analyze the optimal level of decision-making in the economy, a crucial insight for his later work.

Using recent advances in historical national accounts and scholarship of the region, this chapter depicts the economic development of eastern Europe from the times of the Habsburg monarchy and the Russian Empire before World War I through the turbulent interwar years, with the onset of the communist regime and socialist experiment in Russia, the spread of the centrally planned economic system and communist rule in the post-World War II decades, its collapse in the late 1980s and then a decade of transition to market economies. The chapter assesses the region’s economic performance in each of these periods. Even though the path of eastern Europe since the late nineteenth century has been one marked by a series of significant shocks, there were also significant continuities, so that the socialist ‘experiment’ did not result in the significant break with the past that its architects had envisaged.

Paternalist policymakers face a severe knowledge problem that is analogous to the knowledge problem faced by central planners. They do not and often cannot possess the kind of local and tacit knowledge needed to craft policy interventions that reliably improve human welfare. We provide a taxonomy of types of knowledge that paternalist planners need but typically do not have: true preferences, extent of bias, self-debiasing and small-group debiasing, dynamic impacts on self-regulation, counteracting behaviors, bias interactions, and population heterogeneity. We also critique two leading efforts to surmount knowledge problems of behavioral paternalism: the augmented revelatory frame approach and unified behavioral revealed preference.