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This chapter deals with how microeconomics can provide insights into the key challenge that artificial intelligence (AI) scientists face. This challenge is to create intelligent, autonomous agents that can make rational decisions. In this challenge, they confront two questions: what decision theory to follow and how to implement it in AI systems. This chapter provides answers to these questions and makes three contributions. The first is to discuss how economic decision theory – expected utility theory (EUT) – can help AI systems with utility functions to deal with the problem of instrumental goals, the possibility of utility function instability, and coordination challenges in multiactor and human–agent collective settings. The second contribution is to show that using EUT restricts AI systems to narrow applications, which are “small worlds” where concerns about AI alignment may lose urgency and be better labeled as safety issues. The chapter’s third contribution points to several areas where economists may learn from AI scientists as they implement EUT.
Sampling – using a stochastically drawn subset of possibilities – has been at the core of many influential modeling frameworks of human decision making for the last half century. Although these frameworks all refer to their core operation as “sampling,” they differ dramatically in the behaviors and inferences they aim to account for. Here we review this landscape of sampling models under a unified expected utility framework which treats diverse sampling accounts as approximating different terms in the expected utility calculation. We show that a broad range of sample-based models in psychology are built around sampled data, beliefs, or actions and can therefore support downstream expected utility maximization. To compare these models on an even footing, our review focuses on how the number of samples and the sample distribution differ within each element of the expected utility calculation. This integrated summary allows us to identify opportunities for fruitful cross-pollination across sampling domains, and to highlight outstanding challenges for accounts that might aim to integrate these disparate models.
Mainstream economics portrays individual agents as choosing rationally. Many of its generalizations concerning how people actually choose are also claims about how agents ought rationally to choose. Chapter 1 focuses on the conception of rationality that is incorporated in contemporary economics and is central to it. It begins with the concept of preferences, which is the central concept in mainstream economics, and with the theory of rationality that focuses on preferences. The fact that a normative theory lies at the foundation of economics raises philosophical questions. What are requirements of rationality doing in what purports to be a scientific theory of economic phenomena? After presenting the axioms of ordinal utility theory, it offers an account of preferences, a critique of revealed preference theory, and an introduction to expected utility theory. It argues that if one wants to understand economics, the modeling of rationality is the place to begin.
The generalized risk-adjusted cost-effectiveness (GRACE) analysis method modifies standard cost-effectiveness analysis (CEA), the primary method currently used worldwide to value health improvements arising from healthcare interventions. Generalizing standard CEA, GRACE allows for decreasing or even increasing returns to health. Previous presentations of GRACE have relied extensively on Taylor Series expansion methods to specify key model parameters, including those that properly adjust for illness severity and preexisting disability, consequences of uncertain treatment outcomes, and the marginal rate of substitution between life expectancy and health-related quality of life. Standard CEA cannot account for these sources of value or cost in its valuation of medical treatments. However, calculations of GRACE measures based on Taylor Series are approximations, which may be poorly behaved in some contexts. This paper provides a new approach for implementing GRACE, using exact utility functions instead of Taylor Series approximations. While any proper utility function will suffice, we illustrate with three well-known functions: constant relative risk aversion (CRRA) utility; hyperbolic absolute risk aversion (HARA) utility, of which CRRA is a special case; and expo-power (EP) utility, of which constant absolute risk aversion (CARA) is a special case. The analysis then extends from two-period to multiperiod models. We discuss methods to estimate parameters of HARA and EP functions using two different types of data, one from discrete choice experiments and the other from “happiness economics” methods. We conclude with some reflections on how this analysis might affect benefit-cost analysis studies of healthcare interventions.
A new theory of decision-making under risk, the Opportunity-Threat Theory is proposed. Analysis of risk into opportunity and threat components allows description of behavior as a combination of opportunity seeking and threat aversion. Expected utility is a special case of this model. The final evaluation is an integration of the impacts of opportunity and threat with this expectation. The model can account for basic results as well as several “new paradoxes” that refuted cumulative prospect theory in favor of configural weight models. The discussion notes similarities and differences of this model to the configural weight TAX model, which can also account for the new paradoxes.
Humans and other animals are idiosyncratically sensitive to risk, either preferring or avoiding options having the same value but differing in uncertainty. Many explanations for risk sensitivity rely on the non-linear shape of a hypothesized utility curve. Because such models do not place any importance on uncertainty per se, utility curve-based accounts predict indifference between risky and riskless options that offer the same distribution of rewards. Here we show that monkeys strongly prefer uncertain gambles to alternating rewards with the same payoffs, demonstrating that uncertainty itself contributes to the appeal of risky options. Based on prior observations, we hypothesized that the appeal of the risky option is enhanced by the salience of the potential jackpot. To test this, we subtly manipulated payoffs in a second gambling task. We found that monkeys are more sensitive to small changes in the size of the large reward than to equivalent changes in the size of the small reward, indicating that they attend preferentially to the jackpots. Together, these results challenge utility curve-based accounts of risk sensitivity, and suggest that psychological factors, such as outcome salience and uncertainty itself, contribute to risky decision-making.
The body of literature on the relationship between risk aversion and wealth is extensive. However, little attention has been given to examining how future realizations of wealth might affect (current) risk decisions. Using paired lottery choice experiments and exposing subjects experimentally to imagined future wealth frames, I find that individuals are more risk-seeking if they are asked to imagine that they will be wealthy in the future. Yet I find that individuals are not significantly more risk-averse if they are asked to imagine that they will be poor in the future. I discuss theoretical and policy implications of these findings, including why savings rates are so low in the United States.
This chapter looks at Bayesian approaches to cognitive science. The first section reviews the basic elements of conditional probability and Bayes's rule. The second section explores how Bayesian inference might work in the case of perception, which continuously predicts the outside environment. Sensory inputs provide the evidence so that the perception system derives the conditional probability of different hypotheses, given the current evidence, through Bayes's rule, which allows the perception system to update its hypothesis about the environment. We will look at the case of binocular rivalry to see how this inference can work on ambiguous stimuli. In the next section, we address an extension of Bayesian principles to decision-making -- the theory of expected utility. Utility represents the strength of preference for available options. We introduce the calculation of expected utility and look at some experiments suggesting that the brain processes expected utility in a broadly Bayesian manner.
Risk-weighted expected utility theory (REU theory for short) permits preferences which violate the Sure-Thing Principle (STP for short). But preferences that violate the STP can lead to bad decisions in sequential choice problems. In particular, they can lead decision-makers to adopt a strategy that is dominated – i.e. a strategy such that some available alternative leads to a better outcome in every possible state of the world.
I have claimed that risk-weighted expected utility (REU) maximizers are rational, and that their preferences cannot be captured by expected utility (EU) theory. Richard Pettigrew and Rachael Briggs have recently challenged these claims. Both authors argue that only EU-maximizers are rational. In addition, Pettigrew argues that the preferences of REU-maximizers can indeed be captured by EU theory, and Briggs argues that REU-maximizers lose a valuable tool for simplifying their decision problems. I hold that their arguments do not succeed and that my original claims still stand. However, their arguments do highlight some costs of REU theory.
The history of the psychological study of decision-making has its roots in modern theory of probability in the seventeenth century. Here, we describe the historical evolution of ideas from a conception of decision-making as rational to one that is biased and emotional. The historical periods can be divided into two parts, roughly before and after the 1950s. Before the 1950s, decision-making in the mind was thought to reflect pure mathematics. After the 1950s, the weaknesses and inconsistencies of human decision-making became more and more obvious. Ultimately, the elegance of pure mathematics was rejected in favor of theories that captured the messy irrationality of the human mind. The most recent theory shows how biases and emotionality can be part and parcel of advanced decision processes that involve intuitive gist. Thus, modern theory has begun to move beyond Cartesian dualism to encompass cognition, emotion, personality, and social values to predict decision-making.
In this paper we investigate risk-sensitive semi-Markov decision processes with a Borel state space, unbounded cost rates, and general utility functions. The performance criteria are several expected utilities of the total cost in a finite horizon. Our analysis is based on a type of finite-horizon occupation measure. We express the distribution of the finite-horizon cost in terms of the occupation measure for each policy, wherein the discount is not needed. For unconstrained and constrained problems, we establish the existence and computation of optimal policies. In particular, we develop a linear program and its dual program for the constrained problem and, moreover, establish the strong duality between the two programs. Finally, we provide two special cases of our results, one of which concerns the discrete-time model, and the other the chance-constrained problem.
This paper analyzes optimal risk sharing among agents that are endowed with either expected utility preferences or with dual utility preferences. We find that Pareto optimal risk redistributions and the competitive equilibria can be obtained via bargaining with a hypothetical representative agent of expected utility maximizers and a hypothetical representative agent of dual utility maximizers. The representative agent of expected utility maximizers resembles an average risk-averse agent, whereas representative agent of dual utility maximizers resembles an agent that has lowest aversion to mean-preserving spreads. This bargaining leads to an allocation of the aggregate risk to both groups of agents. The optimal contract for the expected utility maximizers is proportional to their allocated risk, and the optimal contract for the dual utility maximizing agents is given by “tranching” of their allocated risk. We show a method to derive equilibrium prices. We identify a condition under which prices are locally independent of the expected utility functions, and given in closed form. Moreover, we characterize uniqueness of the competitive equilibrium.
A microcomputer program to perform Generalized Stochastic Dominance (GSD), Quasi-Second Degree Dominance (SSD), and Quasi-First Degree Stochastic Dominance (FSD) is described. The program is designed to run on IBM-compatible personal computers with a Hercules or CGA graphics adapter. It is menu-driven and has options for GSD, quasi-FSD, quasi-SSD, graphics, and calculations of premiums associated with use of dominant distributions.
In this paper, consideration is given to the normative use of expected-utility theory for the purposes of asset allocation by the trustees of retirement funds. A distinction is drawn between “type-1 prudence”, which relates to deliberate conservatism on the part of actuaries in the setting of assumptions and the determination of model parameters, and “type-2 prudence”, which relates to the risk aversion of the trustees. The intention of the research was to quantify type-2 prudence for the purposes of asset allocation, both for defined-contribution (DC) and defined-benefit (DB) funds. The authors propose new definitions of the objective variables used as the argument of the utility function: one for DC funds and another for DB funds. A new class of utility functions, referred to as the “weighted average relative risk aversion” class is proposed. Practicalities of implementation are discussed. Illustrative results of the application of the method are presented, and it is shown that the proposed approach resolves the paradox of counter-intuitive results found in the literature regarding the sensitivity of the optimal asset allocation to the funding level of a DB fund.
Two important developments in recent policy analysis are behavioral economics and subjective-well-being (SWB) surveys. What is the connection between them? Some have suggested that behavioral economics strengthens the case for SWB surveys as a central policy tool, e.g., in the form of SWB-based cost-benefit analysis. This article reaches a different conclusion. Behavioral economics shows that individuals in their day-to-day, “System 1” behavior are not expected utility (EU-) rational – that they often fail to comply with the norms of rationality set forth by EU theory. Consider now that the standard preference-based view of individual well-being looks to individuals’ rational preferences. If the findings of behavioral economics are correct, an individual’s answer to a question such as “How satisfied are you with your life?” is not going to tell us much about her rational (EU-compliant) preferences. Behavioral economics, by highlighting widespread failures of EU rationality, might actually argue for an objective-good (non-preference-based) view of well-being. However (except in the limiting case of an objective-good view positing a single mentalistic good, happiness), SWB surveys will not be strong evidence of well-being in the objective-good sense. In short, SWB surveys are no “magic cure” for the genuine difficulties in inferring rational preferences and measuring well-being underscored by behavioral economics.
This paper explores the contrast between mentalistic and behaviouristic interpretations of decision theory. The former regards credences and utilities as psychologically real, while the latter regards them as mere representations of an agent's preferences. Philosophers typically adopt the former interpretation, economists the latter. It is argued that the mentalistic interpretation is preferable if our aim is to use decision theory for descriptive purposes, but if our aim is normative then the behaviouristic interpretation cannot be dispensed with.
The most useful and practical strategy available for reducing variability of net farm income is ascertained. Of the many risk management tools presently available, five of the most commonly used are simultaneously incorporated in an empirically tested model. Quadratic programming provides the basis for decisionmaking in risk management wherein expected utility is assumed to be a function of the mean and variance of net income. Results demonstrate that farmers can reduce production and price risks when a combination strategy including a diversified crop production plan and participation in the futures market and the Federal Crop Insurance Program (FCIP) is implemented.
This research evaluates whether the introduction of countercyclical payments creates an incentive for program crop producers to hedge the expected government payment using futures and/or options. Results indicate that some level of countercyclical payment hedging is optimal for risk-averse decision makers. However, optimal hedge ratios depend on planting time expectations of marketing year average price as well as on what crop, if any, has been planted on countercyclical payment base acres. These results suggest that the ability to hedge may make these payments more decoupled but also illustrate the distortion of producer behavior induced by farm programs.
Stocker cattle ownership is compared to contract grazing using stochastic simulation. Returns are evaluated for both cattle owners and caretakers in contract grazing agreements. For caretakers, contract grazing is significantly less risky than cattle ownership. Slightly to moderately risk-averse caretakers could be expected to prefer some type of contract grazing to direct ownership of cattle. For cattle owners, contracting reduces risk only slightly while significantly reducing expected returns.