On the cost of misperceived travel time variability

Recent studies show that traveler’s scheduling preferences compose a willingness-to-pay function directly corresponding to aggregate measurement of travel time variability under some assumptions. This property makes valuation on travel time variability transferable from context to context, which is ideal for extensive policy evaluation. However, if respondents do not exactly maximizing expected utility as assumed, such transferability might not hold because two types of potential errors: (i) scheduling preference elicited from stated preference experiment involving risk might be biased due to misspecification and (ii) ignoring the cost of misperceiving travel time distribution might result in undervaluation. To find out to what extent these errors matter, we reformulate a general scheduling model under rank-dependent utility theory, and derive reduced-form expected cost functions of choosing suboptimal departure time under two special cases. We estimate these two models and calculate the empirical cost due to misperceived travel time variability. We find that (i) travelers are mostly pessimistic and thus tend to choose departure time too earlier to bring optimal cost, (ii) scheduling preference elicited from stated choice method could be quite biased if probability weight- ing is not considered and (iii) the extra cost of misperceiving travel time distribution contributes trivial amount to the discrepancy between scheduling model and its reduced form

Decision by sampling: the role of the decision environment in risky choice

Decision by sampling (DbS) is a theory about how our environment shapes the decisions that we make. Here, I review the application of DbS to risky decision making. According to classical theories of risky decision making, people make stable transformations between outcomes and probabilities and their subjective counterparts using fixed psychoeconomic functions. DbS offers a quite different account. In DbS, the subjective value of an outcome or probability is derived from a series of binary, ordinal comparisons with a sample of other outcomes or probabilities from the decision environment. In this way, the distribution of attribute values in the environment determines the subjective valuations of outcomes and probabilities. I show how DbS interacts with the real-world distributions of gains, losses, and probabilities to produce the classical psychoeconomic functions. I extend DbS to account for preferences in benchmark data sets. Finally, in a challenge to the classical notion of stable subjective valuations, I review evidence that manipulating the distribution of attribute values in the environment changes our subjective valuations just as DbS predicts

Risk Taking of Executives under Different Incentive Contracts: Experimental Evidence

Classic financial agency theory recommends compensation through stock options rather than shares to induce risk neutrality in otherwise risk averse agents. In an experiment, we find that subjects acting as executives do also take risks that are excessive from the perspective of shareholders if compensated through options. Compensation through restricted company stock reduces the uptake of excessive risks. Even under stock-ownership, however, experimental executives continue to take excessive risks—a result that cannot be accounted for by classic incentive theory. We develop a basic model in which such risk-taking behavior is explained based on a richer array of risk attitudes derived from Prospect Theory. We use the model to derive hypotheses on what may be driving excessive risk taking in the experiment. Testing those hypotheses, we find that most of them are indeed borne out by the data. We thus conclude that a prospect-theory-based model is more apt at explaining risk attitudes under different compensation regimes than traditional principal-agent models grounded in expected utility theory

Optimal utility and probability functions for agents with finite computational precision

When making economic choices, such as those between goods or gambles, humans act as if their internal representation of the value and probability of a prospect is distorted away from its true value. These distortions give rise to decisions which apparently fail to maximize reward, and preferences that reverse without reason. Why would humans have evolved to encode value and probability in a distorted fashion, in the face of selective pressure for reward-maximizing choices? Here, we show that under the simple assumption that humans make decisions with finite computational precision––in other words, that decisions are irreducibly corrupted by noise––the distortions of value and probability displayed by humans are approximately optimal in that they maximize reward and minimize uncertainty. In two empirical studies, we manipulate factors that change the reward-maximizing form of distortion, and find that in each case, humans adapt optimally to the manipulation. This work suggests an answer to the longstanding question of why humans make “irrational” economic choices

Load Shifting in the Smart Grid: To Participate or Not?

Demand-side management (DSM) has emerged as an important smart grid feature that allows utility companies to maintain desirable grid loads. However, the success of DSM is contingent on active customer participation. Indeed, most existing DSM studies are based on game-theoretic models that assume customers will act rationally and will voluntarily participate in DSM. In contrast, in this paper, the impact of customers' subjective behavior on each other's DSM decisions is explicitly accounted for. In particular, a noncooperative game is formulated between grid customers in which each customer can decide on whether to participate in DSM or not. In this game, customers seek to minimize a cost function that reflects their total payment for electricity. Unlike classical game-theoretic DSM studies which assume that customers are rational in their decision-making, a novel approach is proposed, based on the framework of prospect theory (PT), to explicitly incorporate the impact of customer behavior on DSM decisions. To solve the proposed game under both conventional game theory and PT, a new algorithm based on fictitious player is proposed using which the game will reach an epsilon-mixed Nash equilibrium. Simulation results assess the impact of customer behavior on demand-side management. In particular, the overall participation level and grid load can depend significantly on the rationality level of the players and their risk aversion tendency.Comment: 9 pages, 7 figures, journal, accepte

Estimating Risk Preferences in the Field

We survey the literature on estimating risk preferences using field data. We concentrate our attention on studies in which risk preferences are the focal object and estimating their structure is the core enterprise. We review a number of models of risk preferences—including both expected utility (EU) theory and non-EU models—that have been estimated using field data, and we highlight issues related to identification and estimation of such models using field data. We then survey the literature, giving separate treatment to research that uses individual-level data (e.g., property insurance data) and research that uses aggregate data (e.g., betting market data). We conclude by discussing directions for future research

Optimal funding and investment strategies in defined contribution pension plans under Epstein-Zin utility

A defined contribution pension plan allows consumption to be redistributed from the plan member’s working life to retirement in a manner that is consistent with the member’s personal preferences. The plan’s optimal funding and investment strategies therefore depend on the desired pattern of consumption over the lifetime of the member. We investigate these strategies under the assumption that the member has an Epstein-Zin utility function, which allows a separation between risk aversion and the elasticity of intertemporal substitution, and we also take into account the member’s human capital. We show that a stochastic lifestyling approach, with an initial high weight in equity-type investments and a gradual switch into bond-type investments as the retirement date approaches is an optimal investment strategy. In addition, the optimal contribution rate each year is not constant over the life of the plan but reflects trade-offs between the desire for current consumption, bequest and retirement savings motives at different stages in the life cycle, changes in human capital over the life cycle, and attitude to risk