What are the testable restrictions imposed on the dynamics of an agents belief by the hypothesis of Bayesian rationality, which do not rely on the additional assumption that the agent has an objectively correct prior? In this paper, I argue that ther
e are essentially no such restrictions. I consider an agent who chooses a sequence of actions and an econometrician who observes the agents actions but not her signals and is interested in testing the hypothesis that the agent is Bayesian. I argue that -- absent a priori knowledge on the part of the econometrician on the set of models considered by the agent -- there are almost no observations that would lead the econometrician to conclude that the agent is not Bayesian. This result holds even if the set of actions is sufficiently rich that the agents action fully reveals her belief about the payoff-relevant state and even if the econometrician observes a large number of identical agents facing the same sequence of decision problems.
The inventories carried in a supply chain as a strategic tool to influence the competing firms are considered to be strategic inventories (SI). We present a two-period game-theoretic supply chain model, in which a singular manufacturer supplies produ
cts to a pair of identical Cournot duopolistic retailers. We show that the SI carried by the retailers under dynamic contract is Pareto-dominating for the manufacturer, retailers, consumers, the channel, and the society as well. We also find that retailers SI, however, can be eliminated when the manufacturer commits wholesale contract or inventory holding cost is too high. In comparing the cases with and without downstream competition, we also show that the downstream Cournot duopoly undermines the profits for the retailers, but benefits all others.
This note provides a critical discussion of the textit{Critical Cost-Efficiency Index} (CCEI) as used to assess deviations from utility-maximizing behavior. I argue that the CCEI is hard to interpret, and that it can disagree with other plausible mea
sures of irrational behavior. The common interpretation of CCEI as wasted income is questionable. Moreover, I show that one agent may have more unstable preferences than another, but seem more rational according to the CCEI. This calls into question the (now common) use of CCEI as an ordinal and cardinal measure of degrees of rationality.
This paper studies matching markets in the presence of middlemen. In our framework, a buyer-seller pair may either trade directly or use the services of a middleman; and a middleman may serve multiple buyer-seller pairs. Direct trade between a buyer
and a seller is costlier than a trade mediated by a middleman. For each such market, we examine an associated cooperative game with transferable utility. First, we show that an optimal matching for a matching market with middlemen can be obtained by considering the two-sided assignment market where each buyer-seller pair is allowed to use the mediation service of the middlemen free of charge and attain the maximum surplus. Second, we prove that the core of a matching market with middlemen is always non-empty. Third, we show the existence of a buyer-optimal core allocation and a seller-optimal core allocation. In general, the core does not exhibit a middleman-optimal matching. Finally, we establish the coincidence between the core and the set of competitive equilibrium payoff vectors.
Classic mechanism design often assumes that a bidders action is restricted to report a type or a signal, possibly untruthfully. In todays digital economy, bidders are holding increasing amount of private information about the auctioned items. And due
to legal or ethical concerns, they would demand to reveal partial but truthful information, as opposed to report untrue signal or misinformation. To accommodate such bidder behaviors in auction design, we propose and study a novel mechanism design setup where each bidder holds two kinds of information: (1) private emph{value type}, which can be misreported; (2) private emph{information variable}, which the bidder may want to conceal or partially reveal, but importantly, emph{not} to misreport. We show that in this new setup, it is still possible to design mechanisms that are both emph{Incentive and Information Compatible} (IIC). We develop two different black-box transformations, which convert any mechanism $mathcal{M}$ for classic bidders to a mechanism $mathcal{M}$ for strategically reticent bidders, based on either outcome of expectation or expectation of outcome, respectively. We identify properties of the original mechanism $mathcal{M}$ under which the transformation leads to IIC mechanisms $mathcal{M}$. Interestingly, as corollaries of these results, we show that running VCG with expected bidder values maximizes welfare whereas the mechanism using expected outcome of Myersons auction maximizes revenue. Finally, we study how regulation on the auctioneers usage of information may lead to more robust mechanisms.
A firm has a group of workers, each of whom has varying productivities over a set of tasks. After assigning workers to tasks, the firm must then decide how to distribute its output to the workers. We first consider three compensation rules and variou
s fairness properties they may satisfy. We show that among efficient and symmetric rules: the Egalitarian rule is the only rule that is invariant to ``irrelevant changes in one workers productivity; the Individual Contribution rule is the only rule that is invariant to the removal of workers and their assigned tasks; and the Shapley Value rule is the only rule that, for any two workers, equalizes the impact one worker has on the other workers compensation. We introduce other rules and axioms, and relate each rule to each axiom.
Electronic countermeasures (ECM) against a radar are actions taken by an adversarial jammer to mitigate effective utilization of the electromagnetic spectrum by the radar. On the other hand, electronic counter-countermeasures (ECCM) are actions taken
by the radar to mitigate the impact of electronic countermeasures (ECM) so that the radar can continue to operate effectively. The main idea of this paper is to show that ECCM involving a radar and a jammer can be formulated as a principal-agent problem (PAP) - a problem widely studied in microeconomics. With the radar as the principal and the jammer as the agent, we design a PAP to optimize the radars ECCM strategy in the presence of a jammer. The radar seeks to optimally trade-off signal-to-noise ratio (SNR) of the target measurement with the measurement cost: cost for generating radiation power for the pulse to probe the target. We show that for a suitable choice of utility functions, PAP is a convex optimization problem. Further, we analyze the structure of the PAP and provide sufficient conditions under which the optimal solution is an increasing function of the jamming power observed by the radar; this enables computation of the radars optimal ECCM within the class of increasing affine functions at a low computation cost. Finally, we illustrate the PAP formulation of the radars ECCM problem via numerical simulations. We also use simulations to study a radars ECCM problem wherein the radar and the jammer have mismatched information.
A decision maker (DM) determines a set of reactions that receivers can choose before senders and receivers move in a generalized competitive signaling model with two-sided matching. The DMs optimal design of the unique stronger monotone signaling equ
ilibrium (unique D1 equilibrium) is equivalent to the choice problem of two threshold sender types, one for market entry and the other for pooling on the top. Our analysis sheds light on the impacts of a trade-off between matching efficiency and signaling costs, the relative heterogeneity of receiver types to sender types, and the productivity of the senders action on optimal equilibrium designing.
We study contests where the designers objective is an extension of the widely studied objective of maximizing the total output: The designer gets zero marginal utility from a players output if the output of the player is very low or very high. We mod
el this using two objective functions: binary threshold, where a players contribution to the designers utility is 1 if her output is above a certain threshold, and 0 otherwise; and linear threshold, where a players contribution is linear if her output is between a lower and an upper threshold, and becomes constant below the lower and above the upper threshold. For both of these objectives, we study (1) rank-order allocation contests that use only the ranking of the players to assign prizes and (2) general contests that may use the numerical values of the players outputs to assign prizes. We characterize the optimal contests that maximize the designers objective and indicate techniques to efficiently compute them. We also prove that for the linear threshold objective, a contest that distributes the prize equally among a fixed number of top-ranked players offers a factor-2 approximation to the optimal rank-order allocation contest.
We study the payoffs that can arise under some information structure from an interim perspective. There is a set of types distributed according to some prior distribution and a payoff function that assigns a value to each pair of a type and a belief
over the types. Any information structure induces an interim payoff profile which describes, for each type, the expected payoff under the information structure conditional on the type. We characterize the set of all interim payoff profiles consistent with some information structure. We illustrate our results through applications.
