Yevgeniy Vorobeychik

STRATEGIC INTERACTIONS IN A SUPPLY CHAIN GAME

The TAC 2003 supply‐chain game presented automated trading agents with a challenging strategic problem. Embedded within a high‐dimensional stochastic environment was a pivotal strategic decision about initial procurement of components. Early evidence suggested that the entrant field was headed toward a self‐destructive, mutually unprofitable equilibrium. Our agent, Deep Maize, introduced a preemptive strategy designed to neutralize aggressive procurement, perturbing the field to a more profitable equilibrium; it worked. Not only did preemption improve Deep Maizes profitability, it improved profitability for the whole field. Whereas it is perhaps counterintuitive that action designed to prevent others from achieving their goals actually helps them, strategic analysis employing an empirical game‐theoretic methodology verifies and provides insight about this outcome.

Behavioral Dynamics and Influence in Networked Coloring and Consensus

We report on human-subject experiments on the problems of coloring (a social differentiation task) and consensus (a social agreement task) in a networked setting. Both tasks can be viewed as coordination games, and despite their cognitive similarity, we find that within a parameterized family of social networks, network structure elicits opposing behavioral effects in the two problems, with increased long-distance connectivity making consensus easier for subjects and coloring harder. We investigate the influence that subjects have on their network neighbors and the collective outcome, and find that it varies considerably, beyond what can be explained by network position alone. We also find strong correlations between influence and other features of individual subject behavior. In contrast to much of the recent research in network science, which often emphasizes network topology out of the context of any specific problem and places primacy on network position, our findings highlight the potential importance of the details of tasks and individuals in social networks.

Empirical mechanism design: methods, with application to a supply-chain scenario

Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings. We illustrate our approach with a design task from a supply-chain trading competition. Designers adopted several rule changes in order to deter particular procurement behavior, but the measures proved insufficient. Our empirical mechanism analysis models the relation between a key design parameter and outcomes, confirming the observed behavior and indicating that no reasonable parameter settings would have been likely to achieve the desired effect. More generally, we show that under certain conditions, the estimator of optimal mechanism parameter setting based on empirical data is consistent.

Maintaining Equilibria During Exploration in Sponsored Search Auctions

We introduce an exploration scheme aimed at learning advertiser click-through rates in sponsored search auctions with minimal effect on advertiser incentives. The scheme preserves both the current ranking and pricing policies of the search engine and only introduces one set of parameters which control the rate of exploration. These parameters can be set so as to allow enough exploration to learn advertiser click-through rates over time, but also eliminate incentives for advertisers to alter their currently submitted bids. When advertisers have much more information than the search engine, we show that although this goal is not achievable, incentives to deviate can be made arbitrarily small by appropriately setting the exploration rate. Given that advertisers do not alter their bids, we bound revenue loss due to exploration.

Equilibrium analysis of dynamic bidding in sponsored search auctions

We analyze symmetric pure strategy equilibria in dynamic sponsored search auction games using simulations by restricting the strategies to several in a class introduced by Cary et al. [1]. We show that a particular convergent strategy also exhibits high stability to deviations. On the other hand, a strategy which yields high payoffs to all players is not sustainable in equilibrium play. Additionally, we analyze a repeated game in which each stage is a static complete-information sponsored search game. In this setting, we demonstrate a collusion strategy which yields high payoffs to all players and empirically show it to be sustainable over a range of settings.

Data-Driven Agent-Based Modeling, with Application to Rooftop Solar Adoption

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among agents. However, agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions. We apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. Meanwhile, we construct a second agent-based model, with its parameters calibrated based on mean square error of its fitted aggregate adoption to the ground truth. Our result suggests that our data-driven agent-based approach based on maximum likelihood estimation substantially outperforms the calibrated agent-based model. Seeing advantage over the state-of-the-art modeling methodology, we utilize our agent-based model to aid search for potentially better incentive structures aimed at spurring more solar adoption. Although the impact of solar subsidies is rather limited in our case, our study still reveals that a simple heuristic search algorithm can lead to more effective incentive plans than the current solar subsidies in San Diego County and a previously explored structure. Finally, we examine an exclusive class of policies that gives away free systems to low-income households, which are shown significantly more efficacious than any incentive-based policies we have analyzed to date.

Maintaining equilibria during exploration in sponsored search auctions

We introduce an exploration scheme aimed at learning advertiser click-through rates in sponsored search auctions with minimal effect on advertiser incentives. The scheme preserves both the current ranking and pricing policies of the search engine and only introduces one parameter which controls the rate of exploration. This parameter can be set so as to allow enough exploration to learn advertiser click-through rates over time, but also eliminate incentives for advertisers to alter their currently submitted bids. When advertisers have much more information than the search engine, we show that although this goal is not achievable, incentives to deviate can be made arbitrarily small by appropriately setting the exploration rate. Given that advertisers do not alter their bids, we bound revenue loss due to exploration.

Probabilistic analysis of simulation-based games

The field of game theory has proved to be of great importance in modeling interactions between self-interested parties in a variety of settings. Traditionally, game-theoretic analysis relied on highly stylized models to provide interesting insights about problems at hand. The shortcoming of such models is that they often do not capture vital detail. On the other hand, many real strategic settings, such as sponsored search auctions and supply-chains, can be modeled in high resolution using simulations. Recently, a number of approaches have been introduced to perform analysis of game-theoretic scenarios via simulation-based models. The first contribution of this work is the asymptotic analysis of Nash equilibria obtained from simulation-based models. The second contribution is to derive expressions for probabilistic bounds on the quality of Nash equilibrium solutions obtained using simulation data. In this vein, we derive very general distribution-free bounds, as well as bounds which rely on the standard normality assumptions, and extend the bounds to infinite games via Lipschitz continuity. Finally, we introduce a new maximum-a-posteriori estimator of Nash equilibria based on game-theoretic simulation data and show that it is consistent and almost surely unique.

Behavioral experiments on a network formation game

We report on an extensive series of behavioral experiments in which 36 human subjects collectively build a communication network over which they must solve a competitive coordination task for monetary compensation. There is a cost for creating network links, thus creating a tension between link expenditures and collective and individual incentives. Our most striking finding is the poor performance of the subjects, especially compared to our long series of prior experiments. We demonstrate that the subjects built difficult networks for the coordination task, and compare the structural properties of the built networks to standard generative models of social networks. We also provide extensive analysis of the individual and collective behavior of the subjects, including free riding and factors influencing edge purchasing decisions.

Multidefender Security Games

Current Stackelberg security game models primarily focus on isolated systems in which only one defender is present, despite being part of a more complex system with multiple players. However, many real systems such as transportation networks and the power grid exhibit interdependencies among targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security scenarios, the authors investigate security games with multiple defenders. Unlike most prior analyses, they focus on situations in which each defender must protect multiple targets, so even a single defenders best response decision is, in general, nontrivial. Considering interdependencies among targets, the authors develop a novel mixed-integer linear programming formulation to compute a defenders best response, and approximate Nash equilibria of the game using this formulation. Their analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or underinvest in security.