This paper explores the role of artificial intelligence (AI) within economic institutions, focusing on bounded rationality as understood by Herbert Simon. Artificial Intelligence can do many things in the economy, such as increasing productivity, enhancing innovation, creating new sectors and jobs, and improving living standards. One of the ways that AI can disrupt the economy is by reducing the problem of bounded rationality. AI can help overcome this problem by processing large amounts of data, finding patterns and insights, and making predictions and recommendations. This insight raises the question: can AI overcome planning problems – could it be that central planning is now a viable option for economic organisation? This paper argues that AI does not make central planning viable at either the nation-state level or the firm level, simply because AI cannot resolve the knowledge problem as described by Ludwig von Mises and Friedrich Hayek.

The outbreak of COVID-19 unleashed a severe crisis in society. The suddenness and speed with which the disease spread into a global pandemic makes it an outstanding case for showing how bureaucracy acts in response to a crisis marked by uncertainty and urgency. This article focuses on the role played by the central government bureaucracy in preparing and enacting the Danish government’s response to the COVID-19 crisis. It is based on full access to internal government files related to crisis management during the winter and spring of 2020. These files include memos, e-mails, decision notes, and draft decisions from key civil servants. The article demonstrates the strength of the theory of bounded rationality when it comes to analyzing the interaction between top civil servants and political executives. Moreover, it shows how administrative and political executives can mold a governmental organization to overcome the inertia inherent in bureaucratic organization and procedures.

In the benchmark New Keynesian (NK) model, I introduce the real cost channel to study government spending multipliers and provide simple Markov chain closed-form solutions. This model departs fundamentally from most previous interpretations of the nominal cost channel by flattening the NK Phillips Curve in liquidity traps. At the zero lower bound, I show analytically that following positive government spending shocks, the real cost channel can make inflation rise less than in a model without this channel. This then causes a smaller drop in real interest rates, resulting in a lower output gap multiplier. Finally, I confirm the robustness of the real cost channel’s effect on multipliers using extensions of two models.

In this paper, I develop an algorithmic impossible-worlds model of belief and knowledge that provides a middle ground between models that entail that everyone is logically omniscient and those that are compatible with even the most egregious kinds of logical incompetence. In outline, the model entails that an agent believes (knows) $\phi $ just in case she can easily (and correctly) compute that $\phi $ is true and thus has the capacity to make her actions depend on whether $\phi $. The model thereby captures the standard view that belief and knowledge ground are constitutively connected to dispositions to act. As I explain, the model improves upon standard algorithmic models developed by Parikh, Halpern, Moses, Vardi, and Duc, among other ways, by integrating them into an impossible-worlds framework. The model also avoids some important disadvantages of recent candidate middle-ground models based on dynamic epistemic logic or step logic, and it can subsume their most important advantages.

A substantial experimental literature in behavioral economics and psychology finds that individuals rely on heuristics and cognitive biases when they make decisions. These heuristics and biases impact the choices of individuals from all walks of life, including police officers entrusted with the power to enforce laws. Individuals act within an institutional context. We examine how the institutions that structure American policing interact with the heuristics and biases of individual police officers. We then suggest institutional changes that may result in better performance from boundedly rational police officers.

How do mushroom foragers make safe and efficient decisions under uncertainty, or deal with the genuine risks of misidentification and poisoning? This article is an inquiry into ecological rationality, heuristics, perception, and decision-making in mushroom foraging. By surveying 894 Finnish mushroom foragers, this article illustrates how socially learned rules of thumb and heuristics are used in mushroom foraging, and how simple heuristics are often complemented by more complex and intuitive decision-making. The results illustrate how traditional foraging cultures have evolved precautionary heuristics to deal with uncertainties and poisonous species, and how foragers develop selective attention through experience. The study invites us to consider whether other human foraging cultures might use heuristics similarly, how and why such traditions have culturally evolved, and whether early hunter-gatherers might have used simple heuristics to deal with uncertainty.

The recognition heuristic exploits the basic psychological capacity for recognition in order to make inferences about unknown quantities in the world. In this article, we review and clarify issues that emerged from our initial work (Goldstein & Gigerenzer, 1999, 2002), including the distinction between a recognition and an evaluation process. There is now considerable evidence that (i) the recognition heuristic predicts the inferences of a substantial proportion of individuals consistently, even in the presence of one or more contradicting cues, (ii) people are adaptive decision makers in that accordance increases with larger recognition validity and decreases in situations when the validity is low or wholly indeterminable, and (iii) in the presence of contradicting cues, some individuals appear to select different strategies. Little is known about these individual differences, or how to precisely model the alternative strategies. Although some researchers have attributed judgments inconsistent with the use of the recognition heuristic to compensatory processing, little research on such compensatory models has been reported. We discuss extensions of the recognition model, open questions, unanticipated results, and the surprising predictive power of recognition in forecasting.

This study provides the first comprehensive analysis of individual perceptions of tail risks. It focuses not only on the probability, as has been studied by Nicholas Barberis and others, but also on anticipation of damage. We examine how those perceptions relate to experts’ estimates and publicly available risk information. Behavioural factors—availability bias, threshold models of choice, worry and trust—are found to have a significant impact on risk perceptions. The probability of tail events is overestimated, which is consistent with probability weighting in prospect theory. Potential damage is underestimated, one reason why individuals do not invest in protective measures.

Recent research suggests that people discount or neglect expectations of reciprocity in trust dilemmas. We examine the underlying processes and boundary conditions of this effect, finding that expectations have stronger effects on trust when they are made accessible and when they are provided as objective probabilities (Study 1). Objective expectations have stronger effects when they are based on precise, rather than ambiguous, probabilities (Study 2). We also find that trust decisions differ from individual risk-taking decisions: people are more willing to trust, and expectations have stronger effects on trusting behavior (Study 2). These results show that the availability and ambiguity of expectations shape trust decisions, and that people differentially weight expectations in dilemmas of trust and individual risk-taking.