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Harstad and Selten (this forum) raise interesting questions about the relative promise of optimization models and bounded-rationality models in making progress in economics. This article builds from their analysis by indicating the potential for using neoclassical (broadly defined) optimization models to integrate insights from psychology on the limits to rationality into economics. I lay out an approach to making (imperfect and incremental) improvements over previous economic theory by incorporating greater realism while attempting to maintain the breadth of application, the precision of predictions, and the insights of neoclassical theory. I then discuss how many human limits to full rationality are, in fact, well understood in terms of optimization.

Research in experimental economics has cogently challenged the fundamental precept of neoclassical economics that economic agents optimize. The last two decades have seen elaboration of boundedly rational models that try to move away from the optimization approach, in ways consistent with experimental findings. Nonetheless, the collection of alternative models has made little headway supplanting the dominant paradigm. We delineate key ways in which neoclassical microeconomics holds continuing and compelling advantages over bounded-rationality models, and suggest, via a few examples, the sorts of further, difficult pushes that would be needed to redress this state of affairs. Closer collaboration between theoretic modeling and experiments is clearly seen to be necessary.

Harstad and Selten's article in this forum performs a valuable service by highlighting the dominance of optimization-based models over boundedly rational models in modern microeconomics, and questioning whether optimization-based models are a better way forward than boundedly rational models. This article complements Rabin's response to Harstad and Selten, focusing on modeling strategic behavior. I consider Harstad and Selten's examples and proposed boundedly rational models in the light of modern behavioral economics and behavioral game theory, commenting on the challenges that remain and the most promising ways forward.

This essay explores the early history of Herbert Simon’s principle of bounded rationality in the context of his Artificial Intelligence research in the mid 1950s. It focuses in particular on how Simon and his colleagues at the RAND Corporation translated a model of human reasoning into a computer program, the Logic Theory Machine. They were motivated by a belief that computers and minds were the same kind of thing—namely, information-processing systems. The Logic Theory Machine program was a model of how people solved problems in elementary mathematical logic. However, in making this model actually run on their 1950s computer, the JOHNNIAC, Simon and his colleagues had to navigate many obstacles and material constraints quite foreign to the human experience of logic. They crafted new tools and engaged in new practices that accommodated the affordances of their machine, rather than reflecting the character of human cognition and its bounds. The essay argues that tracking this implementation effort shows that “internal” cognitive practices and “external” tools and materials are not so easily separated as they are in Simon’s principle of bounded rationality—the latter often shaping the dynamics of the former.

Introduction/Main Objectives: This study examines the effect of heuristic behavior on investment decision-making in the long- and short-term during the COVID-19 pandemic in the Indonesian capital market. Background Problems: Traditional finance cannot fully explain how investors behave in the capital market. Investors will tend to use heuristics when making investment decisions because humans have cognitive limitations, as explained in the bounded rationality theory. Especially during the COVID-19 pandemic, investors have shown their irrationality due to the high uncertainty and panic caused by the COVID-19 pandemic. This phenomenon can only be explained by behavioral finance. Novelty: This study examines the effect of bias on the invest­ment decision-making of investors who make long-term and short-term investments. Previous studies only tested the impact of bias directly, without differentiating the length of time of the investment. Research Methods: This study used partial least squares structural equation modelling (PLS-SEM) with WarpPLS tool. Testing the moderating effect was undertaken using multi-group analysis (MGA). Finding/ Results: The results of this study indicate that anchoring and availability bias have a positive effect on investment decision-making, while representativeness bias has no significant impact. Investment time moderates the effect of representativeness bias on irrational investment decision-making, while anchoring and availability bias are not supported. Conclusion: Anchoring and availability heuristics will increase irrational investment decisions, while the effect of representativeness heuristics on irrational investment decisions will decrease when investors make long-term investments.

Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis . The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.

Using a nested multiple-case study of participating ventures, directors, and mentors of eight of the original U.S. accelerators, we explore how accelerators’ program designs influence new ventures’ ability to access, interpret, and process the external information needed to survive and grow. Through our inductive process, we illuminate the bounded-rationality challenges that may plague all ventures and entrepreneurs—not just those in accelerators—and identify the particular organizational designs that accelerators use to help address these challenges, which left unabated can result in suboptimal performance or even venture failure. Our analysis revealed three key design choices made by accelerators—(1) whether to space out or concentrate consultations with mentors and customers, (2) whether to foster privacy or transparency between peer ventures participating in the same program, and (3) whether to tailor or standardize the program for each venture—and suggests a particular set of choices is associated with improved venture development. Collectively, our findings provide evidence that bounded rationality challenges new ventures differently than it does established firms. We find that entrepreneurs appear to systematically satisfice prematurely across many decisions and thus broadly benefit from increasing the amount of external information searched, often by reigniting search for problems that they already view as solved. Our study also contributes to research on organizational sponsors by revealing practices that help or hinder new venture development and to emerging research on the lean start-up methodology by suggesting that startups benefit from engaging in deep consultative learning prior to experimentation.

When making choices, decision makers often either lack information about alternatives or lack the cognitive capacity to analyze every alternative. To capture these situations, we formulate a framework to study behavioral search by utilizing the idea of consideration sets. Consumers engage in a dynamic search process. At each stage, they consider only those options in the current consideration set. We provide behavioral postulates characterizing this model. We illustrate how one can identify both search paths and preferences.

This paper extends the benchmark New-Keynesian model by introducing two frictions: (i) agent heterogeneity with incomplete markets, uninsurable idiosyncratic risk, and occasionally-binding borrowing constraints; and (ii) bounded rationality in the form of level-k thinking. Compared to the benchmark model, we show that the interaction of these two frictions leads to a powerful mitigation of the effects of monetary policy, which is more pronounced at long horizons, and offers a potential rationalization of the “forward guidance puzzle.” Each of these frictions, in isolation, would lead to no or much smaller departures from the benchmark model.

We study an interactive framework that explicitly allows for nonrational behavior. We do not place any restrictions on how players’ behavior deviates from rationality, but rather, on players’ higher-order beliefs about the frequency of such deviations. We assume that there exists a probability p such that all players believe, with at least probability p , that their opponents play rationally. This, together with the assumption of a common prior, leads to what we call the set of p -rational outcomes, which we define and characterize for arbitrary probability p . We then show that this set varies continuously in p and converges to the set of correlated equilibria as p approaches 1, thus establishing robustness of the correlated equilibrium concept to relaxing rationality and common knowledge of rationality. The p -rational outcomes are easy to compute, also for games of incomplete information. Importantly, they can be applied to observed frequencies of play for arbitrary normal-form games to derive a measure of rationality p ¯ that bounds from below the probability with which any given player chooses actions consistent with payoff maximization and common knowledge of payoff maximization.