Auctions with Heterogeneous Items and Budget Limits

We study individual rational, Pareto optimal, and incentive compatible mechanisms for auctions with heterogeneous items and budget limits. For multi-dimensional valuations we show that there can be no deterministic mechanism with these properties for divisible items. We use this to show that there can also be no randomized mechanism that achieves this for either divisible or indivisible items. For single-dimensional valuations we show that there can be no deterministic mechanism with these properties for indivisible items, but that there is a randomized mechanism that achieves this for either divisible or indivisible items. The impossibility results hold for public budgets, while the mechanism allows private budgets, which is in both cases the harder variant to show. While all positive results are polynomial-time algorithms, all negative results hold independent of complexity considerations

Budget Feasible Mechanisms for Experimental Design

In the classical experimental design setting, an experimenter E has access to a population of $n$ potential experiment subjects $i\in \{1,...,n\}$, each associated with a vector of features $x_i\in R^d$. Conducting an experiment with subject $i$ reveals an unknown value $y_i\in R$ to E. E typically assumes some hypothetical relationship between $x_i$'s and $y_i$'s, e.g., $y_i \approx \beta x_i$, and estimates $\beta$ from experiments, e.g., through linear regression. As a proxy for various practical constraints, E may select only a subset of subjects on which to conduct the experiment. We initiate the study of budgeted mechanisms for experimental design. In this setting, E has a budget $B$. Each subject $i$ declares an associated cost $c_i >0$ to be part of the experiment, and must be paid at least her cost. In particular, the Experimental Design Problem (EDP) is to find a set $S$ of subjects for the experiment that maximizes V(S) = \log\det(I_d+\sum_{i\in S}x_i\T{x_i}) under the constraint $\sum_{i\in S}c_i\leq B$; our objective function corresponds to the information gain in parameter $\beta$ that is learned through linear regression methods, and is related to the so-called $D$-optimality criterion. Further, the subjects are strategic and may lie about their costs. We present a deterministic, polynomial time, budget feasible mechanism scheme, that is approximately truthful and yields a constant factor approximation to EDP. In particular, for any small $\delta > 0$ and $\epsilon > 0$, we can construct a (12.98, $\epsilon$)-approximate mechanism that is $\delta$-truthful and runs in polynomial time in both $n$ and $\log\log\frac{B}{\epsilon\delta}$. We also establish that no truthful, budget-feasible algorithms is possible within a factor 2 approximation, and show how to generalize our approach to a wide class of learning problems, beyond linear regression

Eyewitness identification performance on showups improves with an additional-opportunities instruction: Evidence for present–absent criteria discrepancy

We tested the proposition that when eyewitnesses find it difficult to recognize a suspect (as in a culprit-absent showup), eyewitnesses accept a weaker match to memory for making an identification. We tie this proposition to the basic recognition memory literature, which shows people use lower decision criteria when recognition is made difficult so as to not miss their chance of getting a hit on the target. We randomly assigned participant–witnesses (N = 610) to a condition in which they were told that if they did not believe the suspect was the culprit, they would have additional opportunities to make an identification later (additional-opportunities instruction). We fully crossed this instruction with the standard admonition (i.e., the culprit may or may not be present) and with the presence or absence of the culprit in a showup identification procedure. The standard admonition had no impact on eyewitness decision-making; however, the additional-opportunities instruction reduced innocent-suspect identifications (from 33% to 15%) to a greater extent than culprit identifications (57% to 51%). The additional-opportunities instruction yielded a better tradeoff between culprit and innocent-suspect identifications as indicated by binary logistic regression and receiver operator characteristic (ROC) analyses

Quantum Games and Quantum Strategies

We investigate the quantization of non-zero sum games. For the particular case of the Prisoners' Dilemma we show that this game ceases to pose a dilemma if quantum strategies are allowed for. We also construct a particular quantum strategy which always gives reward if played against any classical strategy.Comment: 4 pages, 4 figures, typographic sign error in the definition of the operator J correcte

Biology helps you to win a game

We present a game of interacting agents which mimics the complex dynamics found in many natural and social systems. These agents modify their strategies periodically, depending on their performances using genetic crossover mechanisms, inspired by biology. We study the performances of the agents under different conditions, and how they adapt themselves. In addition the dynamics of the game is investigated.Comment: 4 pages including 6 figures. Uses REVTeX4. Submitted for Conference Proceedings of the "Unconventional Applications of Statistical Physics", Kolkat

Charge Transport in the Dense Two-Dimensional Coulomb Gas

The dynamics of a globally neutral system of diffusing Coulomb charges in two dimensions, driven by an applied electric field, is studied in a wide temperature range around the Berezinskii-Kosterlitz-Thouless transition. I argue that the commonly accepted ``free particle drift'' mechanism of charge transport in this system is limited to relatively low particle densities. For higher densities, I propose a modified picture involving collective ``partner transfer'' between bound pairs. The new picture provides a natural explanation for recent experimental and numerical findings which deviate from standard theory. It also clarifies the origin of dynamical scaling in this context.Comment: 4 pages, RevTeX, 2 eps figures included; some typos corrected, final version to be published in Phys. Rev. Let

Bargaining Power and Value Sharing in Distribution Networks: A Cooperative Game Theory Approach

This paper illustrates a methodology for analyzing bargaining games on network markets, by means of numerical models that can be calibrated with real data. Economic incentives to join or to expand a network depend on how the network surplus is being distributed, which in turn depends on a variety of factors: position of each agent (e.g., a country) in a specific network, its reliability in the cooperation scheme (e.g., geo-political stability), existence of market distortions and availability of outside options (e.g., alternative energy sources). This study is aimed at presenting a game theory methodology that can be applied to real world cases, having the potential to shed light on several political economy issues. The methodology is presented and illustrated with application to a fictitious network structure. The method is based on a two-stage pro- cess: first, a network optimization model is used to generate payoff values under different coalitions and network structures; a second model is subsequently employed to identify cooperative game solutions. Any change in the network structure entails both a variation in the overall welfare level and in the distribution of surplus among agents, as it affects their relative bargaining power. Therefore, expected costs and benefits, at the aggregate as well as at the individual level, can be compared to assess the economic viability of any investment in network infrastructure. A number of model variants and extensions are also considered: changing demand, exogenous instability factors, market distortions, externalities and outside options

A New Approach to the Design of Electronic Exchanges

Electronic Exchanges are double-sided marketplaces that allows multiple buyers to trade with multiple sellers, with aggregation of demand and supply across the bids to maximize the revenue in the market. In this paper, we propose a new design approach for an one-shot exchange that collects bids from buyers and sellers and clears the market at the end of the bidding period. The main principle of the approach is to decouple the allocation from pricing. It is well known that it is impossible for an exchange with voluntary participation to be efficient and budget-balanced. Budget-balance is a mandatory requirement for an exchange to operate in profit. Our approach is to allocate the trade to maximize the reported values of the agents. The pricing is posed as payoff determination problem that distributes the total payoff fairly to all agents with budget-balance imposed as a constraint. We devise an arbitration scheme by axiomatic approach to solve the payoff determination problem using the added-value concept of game theory

Evolution of Cooperation and Coordination in a Dynamically Networked Society

Situations of conflict giving rise to social dilemmas are widespread in society and game theory is one major way in which they can be investigated. Starting from the observation that individuals in society interact through networks of acquaintances, we model the co-evolution of the agents' strategies and of the social network itself using two prototypical games, the Prisoner's Dilemma and the Stag Hunt. Allowing agents to dismiss ties and establish new ones, we find that cooperation and coordination can be achieved through the self-organization of the social network, a result that is non-trivial, especially in the Prisoner's Dilemma case. The evolution and stability of cooperation implies the condensation of agents exploiting particular game strategies into strong and stable clusters which are more densely connected, even in the more difficult case of the Prisoner's Dilemma.Comment: 18 pages, 14 figures. to appea