Beliefs in Decision-Making Cascades

This work explores a social learning problem with agents having nonidentical noise variances and mismatched beliefs. We consider an $N$-agent binary hypothesis test in which each agent sequentially makes a decision based not only on a private observation, but also on preceding agents' decisions. In addition, the agents have their own beliefs instead of the true prior, and have nonidentical noise variances in the private signal. We focus on the Bayes risk of the last agent, where preceding agents are selfish. We first derive the optimal decision rule by recursive belief update and conclude, counterintuitively, that beliefs deviating from the true prior could be optimal in this setting. The effect of nonidentical noise levels in the two-agent case is also considered and analytical properties of the optimal belief curves are given. Next, we consider a predecessor selection problem wherein the subsequent agent of a certain belief chooses a predecessor from a set of candidates with varying beliefs. We characterize the decision region for choosing such a predecessor and argue that a subsequent agent with beliefs varying from the true prior often ends up selecting a suboptimal predecessor, indicating the need for a social planner. Lastly, we discuss an augmented intelligence design problem that uses a model of human behavior from cumulative prospect theory and investigate its near-optimality and suboptimality.Comment: final version, to appear in IEEE Transactions on Signal Processin

Beliefs and expertise in sequential decision making

This work explores a sequential decision making problem with agents having diverse expertise and mismatched beliefs. We consider an N-agent sequential binary hypothesis test in which each agent sequentially makes a decision based not only on a private observation, but also on previous agents’ decisions. In addition, the agents have their own beliefs instead of the true prior, and have varying expertise in terms of the noise variance in the private signal. We focus on the risk of the last-acting agent, where precedent agents are selfish. Thus, we call this advisor(s)-advisee sequential decision making. We first derive the optimal decision rule by recursive belief update and conclude, counterintuitively, that beliefs deviating from the true prior could be optimal in this setting. The impact of diverse noise levels (which means diverse expertise levels) in the two-agent case is also considered and the analytical properties of the optimal belief curves are given. These curves, for certain cases, resemble probability weighting functions from cumulative prospect theory, and so we also discuss the choice of Prelec weighting functions as an approximation for the optimal beliefs, and the possible psychophysical optimality of human beliefs. Next, we consider an advisor selection problem where in the advisee of a certain belief chooses an advisor from a set of candidates with varying beliefs. We characterize the decision region for choosing such an advisor and argue that an advisee with beliefs varying from the true prior often ends up selecting a suboptimal advisor, indicating the need for a social planner. We close with a discussion on the implications of the study toward designing artificial intelligence systems for augmenting human intelligence.https://arxiv.org/abs/1812.04419First author draf

Strategic Communication Between Prospect Theoretic Agents over a Gaussian Test Channel

In this paper, we model a Stackelberg game in a simple Gaussian test channel where a human transmitter (leader) communicates a source message to a human receiver (follower). We model human decision making using prospect theory models proposed for continuous decision spaces. Assuming that the value function is the squared distortion at both the transmitter and the receiver, we analyze the effects of the weight functions at both the transmitter and the receiver on optimal communication strategies, namely encoding at the transmitter and decoding at the receiver, in the Stackelberg sense. We show that the optimal strategies for the behavioral agents in the Stackelberg sense are identical to those designed for unbiased agents. At the same time, we also show that the prospect-theoretic distortions at both the transmitter and the receiver are both larger than the expected distortion, thus making behavioral agents less contended than unbiased agents. Consequently, the presence of cognitive biases increases the need for transmission power in order to achieve a given distortion at both transmitter and receiver.Comment: 6 pages, 3 figures, Accepted to MILCOM-2017, Corrections made in the new versio

The Relationship Between Risk Attitudes and Heuristics in Search Tasks: A Laboratory Experiment

Experimental studies of search behavior suggest that individuals stop searching earlier than predicted by the optimal, risk-neutral stopping rule. Such behavior could be generated by two different classes of decision rules: rules that are optimal conditional on utility functions departing from risk neutrality, or heuristics derived from limited cognitive processing capacities and satisfycing. To discriminate among these two possibilities, we conduct an experiment that consists of a standard search task as well as a lottery task designed to elicit utility functions. We find that search heuristics are not related to measures of risk aversion, but to measures of loss aversion

Foundations of Behavioral and Experimental Economics: Daniel Kahneman and Vernon Smith

Advanced information on the Prize in Economic Sciences 2002. Until recently, economics was widely regarded as a non-experimental science that had to rely on observation of real-world economies rather than controlled laboratory experiments. Many commentators also found restrictive the common assumption of a homo oeconomicus motivated by self-interest and capable of making rational decisions. But research in economics has taken off in new directions. A large and growing body of scientific work is now devoted to the empirical testing and modification of traditional postulates in economics, in particular those of unbounded rationality, pure self-interest, and complete self-control. Moreover, today's research increasingly relies on new data from laboratory experiments rather than on more traditional field data, that is, data obtained from observations of real economies. This recent research has its roots in two distinct, but converging, traditions: theoretical and empirical studies of human decision-making in cognitive psychology, and tests of predictions from economic theory by way of laboratory experiments. Today, behavioral economics and experimental economics are among the most active fields in economics, as measured by publications in major journals, new doctoral dissertations, seminars, workshops and conferences. This year's laureates are pioneers of these two fields of research.behavioral economics; experimental economics

Information Processing in Decisions under Risk: Evidence for Compensatory Strategies based on Automatic Processes

Many everyday decisions have to be made under risk and can be interpreted as choices between gambles with different outcomes that are realized with specific probabilities. The underlying cognitive processes were investigated by testing six sets of hypotheses concerning choices, decision times, and information search derived from cumulative prospect theory, decision field theory, priority heuristic and parallel constraint satisfaction models. Our participants completed forty decision tasks of two gambles with two non-negative outcomes each. Information search was recorded using eye-tracking technology. Results for all dependent measures conflict with the prediction of the non-compensatory priority heuristic and indicate that individuals use compensatory strategies. Choice proportions are well predicted by a cumulative prospect theory. Process measures, however, indicate that individuals do not rely on deliberate calculations of weighted sums. Information integration processes seem to be better explained by models that partially rely on automatic processes such as decision field theory or parallel constraint satisfaction models.Risky Decisions, Cumulative Prospect Theory, Decision Field Theory, Priority Heuristic, Parallel Constraint Satisfaction, Eye Tracking, Intuition

The Relationship Between Risk Attitudes and Heuristics in Search Tasks: A Laboratory Experiment

The existing evidence from laboratory experiments suggests that relatively simple heuristics describe observed search behavior better than the optimal stopping rule derived under risk neutrality. Such behavior could be generated by two entirely different classes of decision rules: (i) rules that are optimal conditional on utility functions that depart from risk neutrality or (ii) heuristics that derive from limited cognitive processing capacities and satisfycing. In this paper, we develop and test search models that depart from the standard assumption of risk neutrality in order to distinguish these two possibilities. In our experiment, we present subjects not only with a standard search task, but also with a series of lottery tasks that serve to elicit the shape of their utility functions. We do not find a relationship between behavior in the search task and measures of risk aversion. Our data suggest, however, that loss aversion is important for explaining search behavior.