The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element (FE) model, as commonly used in design and construction, can help make sense of the copious amount of collected sensor data. This paper demonstrates the application of the statistical finite element method (statFEM), which provides a principled means of synthesizing data and physics-based models, in developing a digital twin of a self-sensing structure. As a case study, an instrumented steel railway bridge of $ 27.34\hskip1.5pt \mathrm{m} $ length located along the West Coast Mainline near Staffordshire in the UK is considered. Using strain data captured from fiber Bragg grating sensors at 108 locations along the bridge superstructure, statFEM can predict the “true” system response while taking into account the uncertainties in sensor readings, applied loading, and FE model misspecification errors. Longitudinal strain distributions along the two main I-beams are both measured and modeled during the passage of a passenger train. The statFEM digital twin is able to generate reasonable strain distribution predictions at locations where no measurement data are available, including at several points along the main I-beams and on structural elements on which sensors are not even installed. The implications for long-term structural health monitoring and assessment include optimization of sensor placement and performing more reliable what-if analyses at locations and under loading scenarios for which no measurement data are available.

Political actors face a trade-off when they try to influence the beliefs of voters about the effects of policy proposals. They want to sway voters maximally, yet voters may discount predictions that are inconsistent with what they already hold to be true. Should political actors moderate or exaggerate their predictions to maximize persuasion? I extend the Bayesian learning model to account for confirmation bias and show that only under strong confirmation bias are predictions far from the priors of voters self-defeating. I use a preregistered survey experiment to determine whether and how voters discount predictions conditional on the distance between their prior beliefs and the predictions. I find that voters assess predictions far from their prior beliefs as less credible and, consequently, update less. The paper has important implications for strategic communication by showing theoretically and empirically that the prior beliefs of voters constrain political actors.

We investigate the usage of semantic information for morphological segmentation since words that are derived from each other will remain semantically related. We use mathematical models such as maximum likelihood estimate (MLE) and maximum a posteriori estimate (MAP) by incorporating semantic information obtained from dense word vector representations. Our approach does not require any annotated data which make it fully unsupervised and require only a small amount of raw data together with pretrained word embeddings for training purposes. The results show that using dense vector representations helps in morphological segmentation especially for low-resource languages. We present results for Turkish, English, and German. Our semantic MLE model outperforms other unsupervised models for Turkish language. Our proposed models could be also used for any other low-resource language with concatenative morphology.

Does the importance of the economy change during a government's time in office? Governments arguably become more responsible for current economic conditions as their tenure progresses. This might lead voters to hold experienced governments more accountable for economic conditions. However, voters also accumulate information about governments' competence over time. If voters are Bayesian learners, then this growing stock of information should crowd out the importance of current economic conditions. This article explores these divergent predictions about the relationship between tenure and the economic vote using three datasets. First, using country-level data from a diverse set of elections, the study finds that support for more experienced governments is less dependent on economic growth. Secondly, using individual-level data from sixty election surveys covering ten countries, the article shows that voters' perceptions of the economy have a greater impact on government support when the government is inexperienced. Finally, the article examines a municipal reform in Denmark that assigned some voters to new local incumbents and finds that these voters responded more strongly to the local economy. In conclusion, all three studies point in the same direction: economic voting decreases with time in office.

Growth in total factor productivity (TFP) in the USA has slowed down significantly since the mid-2000s, reminiscent of the productivity slowdown of the 1970s. This paper investigates the implications of a productivity slowdown on macroeconomic variables using a standard real business cycle (RBC) model, extended with regime-switching in trend productivity growth and Bayesian learning regarding the growth regime. I estimate the Markov-switching parameters using US data and maximum-likelihood methods, and compute the model solution using global projection methods. Simulations reveal that, while adding a regime-switching component to the standard RBC setup increases the volatility in the system, further incorporating incomplete information and learning significantly dampens this effect. The dampening is mainly due to the responses of investment and labor in response to a switch in the trend component of TFP growth, which are weaker in the incomplete information case as agents mistakenly place some probability that the observed decline in TFP growth is due to the transient component and not due to a regime switch. The model offers an objective way to infer slowdowns in trend productivity, and suggests that macroeconomic aggregates in the USA are currently close to their potential levels given observed productivity, while counterfactual simulations indicate that the cost of the productivity slowdown to US welfare has been significant.

I study the role of shocks to beliefs combined with Bayesian learning in a standard equilibrium business cycle framework. In particular, I examine how a prior belief arising from the Great Depression may have influenced the macroeconomy during the last 75 years. In the model, households hold twisted beliefs concerning the likelihood and persistence of recession and boom states that are affected by the Great Depression. These initial beliefs are substantially different from the true data generating process and are only gradually unwound during subsequent years. Even though the driving stochastic process for technology is unchanged over the entire period, the nature of macroeconomic performance is altered considerably for many decades before eventually converging to the rational expectations equilibrium. This provides some evidence of the lingering effects of beliefs-twisting events on the behavior of macroeconomic variables.

We investigate the general properties of general Bayesian learning, where “general Bayesian learning” means inferring a state from another that is regarded as evidence, and where the inference is conditionalizing the evidence using the conditional expectation determined by a reference probability measure representing the background subjective degrees of belief of a Bayesian Agent performing the inference. States are linear functionals that encode probability measures by assigning expectation values to random variables via integrating them with respect to the probability measure. If a state can be learned from another this way, then it is said to be Bayes accessible from the evidence. It is shown that the Bayes accessibility relation is reflexive, antisymmetric, and nontransitive. If every state is Bayes accessible from some other defined on the same set of random variables, then the set of states is called weakly Bayes connected. It is shown that the set of states is not weakly Bayes connected if the probability space is standard. The set of states is called weakly Bayes connectable if, given any state, the probability space can be extended in such a way that the given state becomes Bayes accessible from some other state in the extended space. It is shown that probability spaces are weakly Bayes connectable. Since conditioning using the theory of conditional expectations includes both Bayes’ rule and Jeffrey conditionalization as special cases, the results presented generalize substantially some results obtained earlier for Jeffrey conditionalization.

We generalize results of earlier work on learning in Bayesian games by allowing players to make decisions in a nonmyopic fashion. In particular, we address the issue of nonmyopic Bayesian learning with an arbitrary number of bounded rational players, i.e., players who choose approximate best-response strategies for the entire horizon (rather than the current period). We show that, by repetition, nonmyopic bounded rational players can reach a limit full-information nonmyopic Bayesian Nash equilibrium (NBNE) strategy. The converse is also proved: Given a limit full-information NBNE strategy, one can find a sequence of nonmyopic bounded rational plays that converges to that strategy.