Discovering Rational Heuristics for Risky Choice

For computationally limited agents such as humans, perfectly rational decision-making is almost always out of reach. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging. Here, we present a machine learning method that identifies the best heuristics to use in any given situation. To demonstrate the generalizability and accuracy of our method, we compare the strategies it discovers against those used by people across a wide range of multi-alternative risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although they tend to collect too little information and their strategy choices do not always fully exploit the structure of the environment.

Your Money or Your Life

The pandemic offers numerous applications of very basic microeconomics concepts and their extension to other aspects of economic life. It also creates an opportunity for better understanding of how the market works and its effects on the economy and society. Allocation of a scarce resource is the definition of economics. There have been countless examples of scarcity (toilet paper, vaccines). How do we decide how to allocate those goods, especially when the market fails? The pandemic is a classic illustration of tradeoffs. In particular, there are tradeoffs between shutting (or re-opening) the economy and loss of human life; a rational decision would compare the costs and the benefits. Lastly, there are countless examples of the unequal economic effects of the virus and their implications for public policy.

Development of the grain crops supply chain model

The possibility of optimization of the grain crops supply chain taking into account the interests of the farm is investigated in the work. The main trends in the agricultural sector of Ukraine, which directly or indirectly affect the activities of the farm, are analyzed in the work. It is established that the choice of the most optimal means of transport for grain crops transportation allows the farm to reduce the cost of the logistics supply chain, which in turn will lead to increased profitability and expansion of the agricultural sector. The solution to this problem is possible through the modeling of the grain crops logistics supply chain. To form a model of the grain crops supply chain the process of grain delivery as a system that contains many acceptable alternatives for making the most rational decision. The proposed model is presented in form of a "black box" model. The objective function of this model for farms is to make a rational decision on the process of grain delivery in order to maximize profits by optimizing the costs of such supply chain and ensuring the development of farming as an activity. The original variables of this model are: grain crops yields and grain crops humidity; farm’s sown area; harvesting equipment efficiency; own granaries capacity; cost for grain storage in own granary or for elevator service; grain share depending on the storage place; sales price of grain crops: to the internal buyer, at the grain reception center, at the port terminal, and also weather and climate conditions during harvest (favorable / unfavorable conditions). The initial indicators that will characterize the success of the grain delivery process will be the following one: harvest duration, harvest amount, trucking cost, average daily harvest duration, additional costs obtained by managing grain humidity, magnitude of crop losses due to the harvest duration, magnitude of losses due to the grain quality reduction (with mixing, without mixing). The proposed model will consider all these factors and their impact on harvesting, storage and transportation of grain crops, and will help to take into account the costs for farms depending on the situation.

Diversity Interventions in the Classroom: From Resistance to Action

The diversity intervention-resistance to action model is presented along with interviews of biology faculty undertaken to understand how resistance to implementing diversity-enhancing classroom interventions manifests at four specific input points within a rational decision-making process that too often results in inaction.

Behavioural economics and health

Human behaviour is an important determinant of health outcomes around the world. Understanding how people make health-related decisions is therefore essential for explaining health outcomes globally and for developing solutions to leading challenges in global health. Behavioural economics blends theories from economics and psychology to uncover key insights about human decision-making. This chapter describes several prominent theories from behavioural economics and reviews examples of how these theories can be useful in efforts to improve global health outcomes. We begin by reviewing the theory of rational decision-making that features prominently in economics and discuss important policy implications that follow from this theory. We then turn to theories and principles from behavioural economics and draw upon empirical evidence from around the world to highlight actionable behaviour change interventions that can be useful for students of global health and practitioners alike.

Effortful Bayesian updating: A pupil-dilation study

When confronted with new information, rational decision makers should update their beliefs through Bayes’ rule. In economics, however, new information often includes win-loss feedback (profits vs. losses, success vs. failure, upticks vs. downticks). Previous research using a well-established belief-updating paradigm shows that, in this case, reinforcement learning (focusing on past performance) creates high error rates, and increasing monetary incentives fails to elicit higher performance. But do incentives fail to increase effort, or rather does effort fail to increase performance? We use pupil dilation to show that higher incentives do result in increased cognitive effort, but the latter fails to translate into increased performance in this paradigm. The failure amounts to a “reinforcement paradox:” increasing incentives makes win-loss cues more salient, and hence effort is often misallocated in the form of an increased reliance on reinforcement processes. Our study also serves as an example of how pupil-dilation measurements can inform economics.

A rational route to transformative decisions

According to Paul (Transformative experience, 1st edn, Oxford University Press, 2014), transformative experiences pose a challenge to decision theory since their value cannot be anticipated. Building on Pettigrew’s (in: Lambert, Schwenkler (eds) Becoming someone new: essays on transformative experience, choice, and change, Oxford University Press, pp 100–121, 2020) redescription, this paper presents a new approach to how and when transformative decisions can nevertheless be made rationally. Thanks to fundamental higher-order facts that apply to any kind of experience, an agent always at least knows the general shape of the utility space. This in combination with the knowledge about the non-transformative alternative in the choice set can enable rational decision-making despite the presence of a transformative experience. For example, this paper’s approach provides novel arguments for why gender transition (cf. McKinnon in Res Philosophica 92(2):419–440, 2015) or staying childfree (cf. Barnes in Philos Phenomenol Res 91(3):775–786, 2015) can be rational.