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Incentive compatibility is core to mechanism design. The success of auctions, matching algorithms, and voting systems all hinge on the ability to select incentives that make it in the individual's interest to reveal their type. But how do we test whether a mechanism that is designed to be incentive compatible is actually so in practice, particularly when faced with boundedly rational agents with nonstandard preferences? We review the many experimental tests that have been designed to assess behavioral incentive compatibility, separating them into two categories: indirect tests that evaluate behavior within the mechanism, and direct tests that assess how participants respond to the mechanism's incentives. Using belief elicitation as a running example, we show that the most popular elicitations are not behaviorally incentive compatible. In fact, the incentives used under these elicitations discourage rather than encourage truthful revelation.

This paper provides a general analysis of signaling under double-crossing preferences with a continuum of types. There are natural economic environments where the indifference curves of two types cross twice, such that the celebrated single-crossing property fails to hold. Equilibrium exhibits a threshold type below which types choose actions that are fully revealing and above which they pool in a pairwise fashion, with a gap separating the actions chosen by these two sets of types. The resulting signaling action is quasi-concave in type. We also provide an algorithm to establish equilibrium existence by construction.

The paper examines notions of incentive compatibility in an environment with ambiguity-averse agents. In particular, we propose the notion of maxmin transfer coalitional incentive compatibility, which is immune to coalitional manipulations and thus more stable than the individual incentive compatibility condition. The main result characterizes the set of allocations that satisfy the maxmin transfer coalitional incentive compatibility condition. We show that an allocation satisfies maxmin transfer coalitional incentive compatibility if and only if it is maxmin interim efficient. This result extends that of De Castro and Yannelis (J Econ Theory 177:678–707, 2018) in the sense that ambiguity not only resolves the conflict between efficiency and incentive compatibility, it also accommodates stability. Furthermore, this result is false in a finite economy where agents are subjective expected utility maximizers.

We explore the structure of locally ordinal Bayesian incentive compatible (LOBIC) random Bayesian rules (RBRs). We show that under lower contour monotonicity, for almost all prior profiles (with full Lebesgue measure), a LOBIC RBR is locally dominant strategy incentive compatible (LDSIC). We further show that for almost all prior profiles, a unanimous and LOBIC RBR on the unrestricted domain is random dictatorial, and thereby extend the result in Gibbard (Econometrica 45:665–681, 1977) for Bayesian rules. Next, we provide a sufficient condition on a domain so that for almost all prior profiles, unanimous RBRs on it are tops-only. Finally, we provide a wide range of applications of our results on single-peaked (on arbitrary graphs), hybrid, multiple single-peaked, single-dipped, single-crossing, multi-dimensional separable domains, and domains under partitioning. Since OBIC implies LOBIC by definition, all our results hold for OBIC RBRs.

We study the question of whether local incentive constraints are sufficient to imply full incentive compatibility in a variety of mechanism design settings, allowing for probabilistic mechanisms. We give a unified approach that covers both continuous and discrete type spaces. On many common preference domains—including any convex domain of cardinal or ordinal preferences, single-peaked ordinal preferences, and successive single-crossing ordinal preferences—local incentive compatibility (suitably defined) implies full incentive compatibility. On domains of cardinal preferences that satisfy a strong nonconvexity condition, local incentive compatibility is not sufficient. Our sufficiency results hold for dominant-strategy and Bayesian Nash solution concepts, and allow for some interdependence in preferences.

This work contributes an (approximately) incentive-compatible and computationally efficient bargaining mechanism for pricing fog computing resources. In network settings (e.g., fog computing), it is plausible to think that self-interested and incompletely informed players (represented by software agents) will attempt to maximize their own benefits at the expense of others. Hence, it is crucial that fog bargaining mechanisms give incentives to agents for behaving in a manner consistent with the desired outcome where every agent’s benefit is maximized. Equilibrium analyses validate that the fog bargaining mechanism in this work is approximately Bayesian incentive compatible because every agent can approximately maximize its expected utility by adhering to the strategy recommended by the bargaining mechanism given that all other agents also adhere to their equilibrium strategies. That is, if every agent in the market adheres to the strategy recommended by the bargaining mechanism, then the strategy profile of the agents forms an approximate Bayesian Nash equilibrium. Given that a fog resource market has a large number of buyers and a large number of sellers, computational efficiency is also imperative since every agent needs to process a huge number of trading alternatives. Computational complexity analyses validate that 1) the procedure for carrying out the bargaining strategy has a linear time complexity, and with every passing round, the search space dwindles but the solutions become progressively better, 2) the number of rounds for each agent to complete bargaining is logarithmic in the number of its opponents, and 3) each agent has a linear message complexity.

We study a double auction environment where buyers and sellers have interdependent valuations and multi-unit demand and supply. We propose a new mechanism that satisfies ex post incentive compatibility, individual rationality, feasibility, nonwastefulness, and no budget deficit. Moreover, this mechanism is asymptotically efficient in that the trade outcome in the mechanism converges to the efficient level as in a competitive equilibrium as the numbers of the buyers and sellers become large. Our mechanism is the first double auction mechanism with these properties in the interdependent values setting.

It is well-known that the ability of the Vickrey-Clarke-Groves (VCG) mechanism to implement efficient outcomes for private value choice problems does not extend to interdependent value problems. When an agent's type affects other agents' utilities, it may not be incentive compatible for him to truthfully reveal his type when faced with VCG payments. We show that when agents are informationally small, there exist small modifications to the VCG transfers that restore incentive compatibility. We further show that truthful revelation is an approximate ex post equilibrium. Lastly, we show that in replicated settings aggregate payments sufficient to induce truthful revelation go to zero.

We study mechanism design in dynamic quasilinear environments where private information arrives over time and decisions are made over multiple periods. We make three contributions. First, we provide a necessary condition for incentive compatibility that takes the form of an envelope formula for the derivative of an agent's equilibrium expected payoff with respect to his current type. It combines the familiar marginal effect of types on payoffs with novel marginal effects of the current type on future ones that are captured by \"impulse response functions.\" The formula yields an expression for dynamic virtual surplus that is instrumental to the design of optimal mechanisms and to the study of distortions under such mechanisms. Second, we characterize the transfers that satisfy the envelope formula and establish a sense in which they are pinned down by the allocation rule (\"revenue equivalence\"). Third, we characterize perfect Bayesian equilibrium-implementable allocation rules in Markov environments, which yields tractable sufficient conditions that facilitate novel applications. We illustrate the results by applying them to the design of optimal mechanisms for the sale of experience goods (\"bandit auctions\").

This paper aims to propose effective auction mechanisms for the carrier collaboration problem with bilateral exchange, which is generally the problem of how to realize the potential of carrier collaboration over a bilateral exchange transportation network (e.g., a B2B e-commerce logistics environment). Carriers offer the lanes with the highest marginal costs for subcontracting, while they are allowed to bid on bundles of the lanes. We construct a bundle double auction (BDA) for the one-unit demand case in which the lane offered by each buyer (transportation service purchaser) is only required to be covered with one truckload once. The BDA mechanism realizes incentive compatibility, individual rationality, budget balance, and asymptotical efficiency. We then propose two mechanisms, called BDA-1 and BDA-2, for the multiunit demand case in which each buyer asks for one or multiple truckloads of transportation service. Both mechanisms are effective but differ in incentive compatibility and realized social welfare. The computational study shows that all proposed mechanisms are practically implementable and lead to considerable cost savings for the carrier collaboration network, and most of the benefits generated via collaboration are assigned among carriers. We also numerically analyze the impacts of three operational factors: network structure, the degree of self-served lanes, and the number of lanes in the network.