The art of conversation: eliciting information from experts through multi-stage communication

We examine the strategic interaction between an informed expert and an uninformed decision maker, extending the analysis of Crawford and Sobel (Econometrica 50 (1982) 1431). We modify their model to allow for more extensive communication between the two parties and show that face-to-face communication between the expert and the uninformed decision maker followed by a written report from the expert leads to improved information transmission. In (almost) all cases, there exists an equilibrium in our modified model that ex ante Pareto dominates all of the equilibria identified by Crawford and Sobel. This remains true even if the expert's bias is so great that in their model no information would be disclosed.     

Strategic argumentation

I analyze a game between an uninformed decision maker and a possibly biased expert. The expert receives a set of arguments, and each argument favors one of two alternatives. He can disclose each argument credibly, but cannot prove whether he has disclosed everything. In all equilibria, the biased expert sends messages containing arguments both for and against his preferred alternative. However, the decision maker is not influenced by the unfavorable arguments revealed by the biased expert. The latter is able to convince the decision maker to choose the biased expert?s preferred alternative only if he reveals sufficiently many favorable arguments.     

Aggregation of multiple prior opinions

Experts are asked to provide their advice in a situation of uncertainty. They adopt the decision maker?s utility function, but each has a potentially different set of prior probabilities, and so does the decision maker. The decision maker and the experts maximize the minimal expected utility with respect to their sets of priors. We show that a natural Pareto condition is equivalent to the existence of a set Λ of probability vectors over the experts, interpreted as possible allocations of weights to the experts, such that (i) the decision maker?s set of priors is precisely all the weighted-averages of priors, where an expert?s prior is taken from her set and the weight vector is taken from Λ; (ii) the decision maker?s valuation of an act is the minimal weighted valuation, over all weight vectors in Λ, of the experts? valuations.     

When mandatory disclosure hurts: Expert advice and conflicting interests

We study the quality of advice that an informed and biased expert gives to an uninformed decision maker. We compare two scenarios: mandatory disclosure of the bias and nondisclosure, where information about the bias can only be revealed through cheap-talk. We find that in many scenarios nondisclosure allows for higher welfare for both parties. Hiding the bias allows for more precise communication for the more biased type and, if different types are biased in different directions, may allow for the same for the less biased type. We identify contexts where equilibrium revelation allows but mandatory disclosure prevents meaningful communication.     

SEQUENTIAL EXPERT ADVICE: SUPERIORITY OF CLOSED‐DOOR MEETINGS

Two career‐concerned experts sequentially give advice to a Bayesian decision maker (D). We find that secrecy dominates transparency, yielding superior decisions for D. Secrecy empowers the expert moving late to be pivotal more often. Further, (i) only secrecy enables the second expert to partially communicate her information and its high precision to D and swing the decision away from first expert's recommendation; (ii) if experts have high average precision, then the second expert is effective only under secrecy. These results are obtained when experts only recommend decisions. If they also report the quality of advice, fully revealing equilibrium may exist.     

Persuasion,binary choice,and the costs of dishonesty

We study the strategic interaction between a decision maker who needs to take a binary decision but is uncertain about relevant facts and an informed expert who can send a message to the decision maker but has a preference over the decision. We show that the probability that the expert can persuade the decision maker to take the expert’s preferred decision is a hump-shaped function of his costs of sending dishonest messages.     

PARTISAN ADVOCATES

This paper studies the problem of an uninformed decision maker who acquires expert advice prior to making a decision. I show that it is less costly to hire partisan agents than impartial agents, especially under advocacy, and that the decision maker prefers partisan advocacy to other forms of institutions. I also extend the literature, originating with Dewatripont and Tirole ( 1999 ), to a setting with contracts that condition on information provided and not just the decision made.     

Minimax regret with imperfect ex-post knowledge of the state

We consider decision problems under complete ignorance and extend the minimax regret principle to situations where, after taking an action, the decision maker does not necessarily learn the state of the world. For example, if the decision maker only learns what the outcome is, then all she knows is that the actual state is one of the (possibly several) states that yield the observed outcome under the chosen action. We refer to this situation as imperfect ex-post information. We show that, given a choice between more information and less information, the decision maker prefers the latter. We also extend the framework to encompass the possibility of less than the extreme degree of pessimism that characterizes the minimax regret criterion.     

Anti-herding and strategic consultation

In this paper I analyze how careerist decision makers aggregate and use information provided by others. I find that decision makers who are motivated by reputation concerns tend to ‘anti-herding’, i.e., they excessively contradict public information such as the prior or others’ recommendations. I also find that some decision makers may deliberately act unilaterally and not consult advisers although advice is costless. Moreover, advisers to the decision maker may not report their information truthfully. Even if the advisers care only about the outcome, they bias their recommendation since they anticipate inefficient anti-herding behavior by the decision maker.     

The value of a stochastic information structure

Upon observing a signal, a Bayesian decision maker updates her probability distribution over the state space, chooses an action, and receives a payoff that depends on the state and the action taken. An information structure determines the set of possible signals and the probability of each signal given a state. For a fixed decision problem, the value of an information structure is the maximal expected utility that the decision maker can get when the observed signals are governed by this structure. Thus, every decision problem induces a preference order over information structures according to their value. We characterize preference orders that can be obtained in this way. We also characterize the functions defined over information structures that measure their value.     

Optimal voting schemes with costly information acquisition

A group of individuals with identical preferences must make a decision under uncertainty about which decision is best. Before the decision is made, each agent can privately acquire a costly and imperfect signal. We discuss how to design a mechanism for eliciting and aggregating the collected information so as to maximize ex-ante social welfare.We first show that, of all mechanisms, a sequential one is optimal and works as follows. At random, one agent at a time is selected to acquire information and report the resulting signal. Agents are informed of neither their position in the sequence nor of other reports. Acquiring information when called upon and reporting truthfully is an equilibrium.We next characterize the ex-ante optimal scheme among all ex-post efficient mechanisms. In this mechanism, a decision is made when the precision of the posterior exceeds a cut-off that decreases with each additional report. The restriction to ex-post efficiency is shown to be without loss when the available signals are sufficiently imprecise. On the other hand, ex-post efficient mechanisms are shown to be suboptimal when the cost of information acquisition is sufficiently small.     

On the demand for information

Most previous research on the economics of information has been based on the expected utility hypothesis and Bayesian learning. Yet there is experimental evidence that decision makers do not always maximize expected utility and that human learning is not always consistent with Bayes' rule. Using a two-period model, this paper examines the valuation of information as well as the demanf for information in the broader context of a state preference approach under an ordinal representation of preferences. In particular, it is not assumed that the decision maker is Bayesian, nor that he behaves in a way consistent with the expected utility hypothesis. In this general framework, the value of information is defined and analysed. Also, under active learning, the optimal allocation of information gathering activities is discussed. Behavioural properties of the demand for information are derived making use of a compensation function. Implications of the results for measuring the value of information and for the economic analysis of learning activities are discussed.     

Multiple Criteria Decision Making: A Case Study of the Columbia River Salmon Recovery Plan

A common problem faced by decision makers is choosing the best alternative from among many. Traditionally, such decisions in the public arena were made using benefit-cost analysis, which involves the conversion of all costs and benefits associated with a project into monetary terms. But public projects often have a variety of economic, ecological, social and political objectives, many of which cannot or perhaps should not be converted to monetary terms. In such projects decisions must be made based on multiple, even conflicting objectives. Multiple criteria decision making (MCDM) methods are widely used for such decisions. However, a common disadvantage among many such methods available in the literature is that they require input from a real decision maker. This paper presents the development and application of an expert system based on fuzzy set theory and IF-THEN rules. The system mimics a real decision maker. Along with two conventional MCDM methods the developed expert system was applied on a data set from the Columbia River Basin salmon recovery plan to assess its potential usefulness as a decision-making tool for natural resource projects. The results suggest that the fuzzy expert system is easy to develop and makes better decisions than the other two conventional MCDM methods used.     

Partial ownership and cross-border mergers

Partial ownership can be used as a screening device by a foreign firm which wants to merge with a local firm whose productivity is private information. As partial ownership is confined to sharing future merger profits, it cannot achieve complete separation in all cases but improves expected merger gains also in an equilibrium which is not fully separating. Without partial ownership, the foreign firm potentially discriminates against high productivities. In a pooling equilibrium with partial ownership, however, it will potentially discriminate against intermediate productivities.     

Persuasion games with higher-order uncertainty

Shin (J. Econom. Theory 64 (1994) 253–264) showed that a perfectly revealing equilibrium fails to exist in persuasion games when the decision maker is uncertain about the interested party's payoff-relevant information. By explicitly integrating higher-order uncertainty into the information structure, this note shows that a perfectly revealing equilibrium does exist when disclosures are not restrained to intervals of the payoff-relevant state space.     

Harmful lie aversion and lie discovery in noisy expert advice games

This study tests whether individuals are reluctant to tell lies, or perhaps only “harmful lies”, in a previously untested environment: an expert sending a message to a decision maker whose interpretation of that message is subject to error, i.e. a noisy sender–receiver game. In the Aligned treatment, the expert can send a “white lie” to the receiver, eliminating the negative effects of noise and improving both parties’ payoffs. In the Conflict treatment, lies are harmful and the inability to commit to truthtelling destroys all meaningful communication in equilibrium unless there is a cost of lying. In the experiment, receivers are overly trusting and experts learn to take advantage of this. As experts gain experience they tell stronger and more frequent lies in both treatments, consistent with models of reinforcement learning. The findings suggest that neither harmful nor universal lie aversion is a factor when communication is noisy, provided individuals have time to discover their personal benefits of lies.     

Prior symmetry, similarity-based reasoning, and endogenous categorization

This paper presents a rational theory of categorization and similarity-based reasoning. I study a model of sequential learning in which the decision maker infers unknown properties of an object from information about other objects. The decision maker may use the following heuristics: divide objects into categories with similar properties and predict that a member of a category has a property if some other member of this category has this property. The environment is symmetric: the decision maker has no reason to believe that the objects and properties are a priori different. In symmetric environments, categorization is an optimal solution to an inductive inference problem. Any optimal solution looks as if the decision maker categorizes. Various experimental observations about similarity-based reasoning coincide with the optimal behavior in my model.     

The rationality and efficacy of decisions under uncertainty and the value of an experiment

Summary A decision maker faces a known prior distribution over payoff relevant states. We compare the expected utility of this individual under two scenarios. In the first, the decision maker makes a choice without further information. In the second, the decision maker has access to an experiment before choosing an action. However, the decision maker does not know the true joint distribution over states and messages. The value of the experiment as measured by the difference in the two utility levels can be negative as well as positive. We give a condition which is necessary and sufficient for the experiment to be valuable in our sense, for any decision problem.An earlier version of this paper was circulated under the title Noisy Bayes Updating and the Value of Information. We have gained from the comments of Stephen Coate, John Geanakoplos, Larry Samuelson, Timothy Van Zandt and seminar participants at Harvard Business School, Princeton, Boston University, the international conference on game theory at Stony Brook 1992 and the Winter meeting of the Econometric Society at Anaheim 1993. The first author received support for this project from NSF grant #SES-9308515 and a University of Pennsylvania Research Foundation Grant.     

Comparative cheap talk

When are comparative statements credible? We show that simple complementarity conditions ensure that an expert with private information about multiple issues can credibly rank the issues for a decision maker. By restricting the expert's ability to exaggerate, multidimensional cheap talk of this form permits communication when it would not be credible in a single dimension. The communication gains can be substantial with even a couple of dimensions, and the complete ranking is asymptotically equivalent to full revelation as the number of issues becomes large. Nevertheless, partial rankings are sometimes more credible and/or more profitable for the expert than the complete ranking. Comparative cheap talk is robust to asymmetries that are not too large. Consequently, for sufficiently many independent issues, there are always some issues sufficiently symmetric to permit comparative cheap talk.     

The existence of fully rational expectations approximate equilibria with noisy price observations

Nonrevealing fully rational expectations approximate equilibria exist in microeconomic pure exchange economies in which uninformed agents have suitably dispersed noisy price observations. Such traders maximize a state-dependent expected utility conditional on the price vector they observe, the distributions of noisy price observations, and the correct equilibrium relationship between states of the world and prices. In equilibrium, aggregate excess demand is small with high probability in every state of the world (and its expectation is also small); this magnitude diminishes as the noisy price observations become more accurate. Equilibria are obtained by applying a fixed point argument to state-dependent excess demand functions which are smooth because of the noisy price observations.