Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data

We consider learning, from strictly behavioral data, the structure and parameters of linear influence games (LIGs), a class of parametric graphical games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic inference (CSI): Making inferences from causal interventions on stable behavior in strategic settings. Applications include the identification of the most influential individuals in large (social) networks. Such tasks can also support policy-making analysis. Motivated by the computational work on LIGs, we cast the learning problem as maximum-likelihood estimation (MLE) of a generative model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation uncovers the fundamental interplay between goodness-of-fit and model complexity: good models capture equilibrium behavior within the data while controlling the true number of equilibria, including those unobserved. We provide a generalization bound establishing the sample complexity for MLE in our framework. We propose several algorithms including convex loss minimization (CLM) and sigmoidal approximations. We prove that the number of exact PSNE in LIGs is small, with high probability; thus, CLM is sound. We illustrate our approach on synthetic data and real-world U.S. congressional voting records. We briefly discuss our learning framework's generality and potential applicability to general graphical games.Comment: Journal of Machine Learning Research. (accepted, pending publication.) Last conference version: submitted March 30, 2012 to UAI 2012. First conference version: entitled, Learning Influence Games, initially submitted on June 1, 2010 to NIPS 201

Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting

We consider the distributed channel selection problem in the context of device-to-device (D2D) communication as an underlay to a cellular network. Underlaid D2D users communicate directly by utilizing the cellular spectrum but their decisions are not governed by any centralized controller. Selfish D2D users that compete for access to the resources construct a distributed system, where the transmission performance depends on channel availability and quality. This information, however, is difficult to acquire. Moreover, the adverse effects of D2D users on cellular transmissions should be minimized. In order to overcome these limitations, we propose a network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels. This scenario is modeled as a multi-player multi-armed bandit game with side information, for which a distributed algorithmic solution is proposed. The solution is a combination of no-regret learning and calibrated forecasting, and can be applied to a broad class of multi-player stochastic learning problems, in addition to the formulated channel selection problem. Analytically, it is established that this approach not only yields vanishing regret (in comparison to the global optimal solution), but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.Comment: 31 pages (one column), 9 figure

Using Surveys to Compare the Public’s and Decisionmakers’ Preferences for Urban Regeneration: The Venice Arsenale

In this paper, we illustrate how surveys can be used to elicit the preferences of the public and of policymakers and city officials for regeneration projects at urban sites. Our methodology uses rating exercises, coupled with conjoint-choice stated preferences for the general public and with ranking exercises for the public officials and other stakeholders, and is then applied to investigate alternative reuses of the Venice Arsenale, Italy, and their economic, environmental and social impacts. One interesting feature of the conjoint choice questions for members of the public is that the responses to these questions can be used to estimate the social benefits of regeneration projects, i.e., how much people are willing to pay for these urban transformations. Another advantage of our approach is that it can be used seek and foster broader public participation into urban decisionmaking processes.Land Use, Decision-Making, Cleanup, Sustainable Development, Local Economic Development, Choice Experiments

Platform Competition with Endogenous Multihoming

A model of two-sided market (for credit cards) is introduced and discussed. In this model, agents can join none, one, or more than one platform (multihoming), depending on access prices and the choices made by agents on the opposite market side. Although emerging multihoming patterns are, clearly, one aspect of equilibrium in a two-sided market, this issue has not yet been thoroughly addressed in the literature. This paper provides a general theoretical framework, in which homing partitions are conceived as one aspect of market equilibrium, rather than being set ex-ante, through ad-hoc assumptions. The emergence of a specific equilibrium partition is a consequence of: (1) the structure of costs and benefits, (2) the degree and type of heterogeneity among agents, (3) the intensity of platform competition.Two-sided markets, Network externalities, Standards, Platforms, Multihoming

Experimental evidence that quorum rules discourage turnout and promote election boycotts

NIPE WP-14/2013In most instances of collective decision-making, it cannot be expected that all persons who are entitled to vote will end up doing so. This has led institutional designers, out of concerns with the “legitimacy” of decisions, to introduce quorum requirements. A prominent example of this can be found in the context of direct democracy mechanisms, such as referenda and initiatives. We discuss the results of an experiment about the consequences of such quora. We show that quora lead to overall decreases in participation rates, dramatically increasing the likelihood of full-fledged electoral boycotts on the part of status quo supporters.COMPETE;QREN;FEDER; Fundação para a Ciência e a Tecnologia (FCT

Care for Elderly Parents: Do Children Cooperate?

Do children cooperate when they decide to provide informal care to their elderly parent? This paper assesses which model drives the caregiving decisions of children. I compare the predictive power of two models: a (joint-utility) cooperative and a Nash noncooperative model. I focus on families with two children and one single parent. The model allows caregiving by one child to have a direct externality on the well-being of the sibling. The results suggest that the cooperative model overestimates the level of care received by the parents observed in the data and its predictive power is outperformed by the noncooperative model. This suggests that children are more likely to behave according to a noncooperative model. I also find that children’s participation in caregiving has a positive externality on the well-being of the sibling. I construct an indicator of the degree of noncooperativeness between children and show that it is positively correlated with the number of unmet needs the parent has. I conclude that, because children do not internalize the positive externality when they behave noncooperatively, the current level of informal care provided to parents appears to suffer from a public good problem

Heckle and Chide: Results of a Randomized Road Safety Intervention in Kenya

In economies with weak enforcement of traffic regulations, drivers who adopt excessively risky behavior impose externalities on other vehicles, and on their own passengers. In light of the difficulties of correcting inter-vehicle externalities associated with weak third-party enforcement, this paper evaluates an intervention that aims instead to correct the intra-vehicle externality between a driver and his passengers, who face a collective action problem when deciding whether to exert social pressure on the driver if their safety is compromised. We report the results of a field experiment aimed at solving this collective action problem, which empowers passengers to take action. Evocative messages encouraging passengers to speak up were placed inside a random sample of over 1,000 long-distance Kenyan minibuses, or matatus, serving both as a focal point for, and to reduce the cost of, passenger action. Independent insurance claims data were collected for the treatment group and a control group before and after the intervention. Our results indicate that insurance claims fell by a half to two-thirds, from an annual rate of about 10 percent without the intervention, and that claims involving injury or death fell by at least 50 percent. Results of a driver survey eight months into the intervention suggest passenger heckling was a contributing factor to the improvement in safety.Kenya, traffic, driving regulations, matatus, safety