Understanding the nature of strategic voting is the holy grail of social choice theory, where game-theory, social science and recently computational approaches are all applied in order to model the incentives and behavior of voters. In a recent paper, Meir et al.[EC'14] made another step in this direction, by suggesting a behavioral game-theoretic model for voters under uncertainty. For a specific variation of best-response heuristics, they proved initial existence and convergence results in the Plurality voting system. This paper extends the model in multiple directions, considering voters with different uncertainty levels, simultaneous strategic decisions, and a more permissive notion of best-response. It is proved that a voting equilibrium exists even in the most general case. Further, any society voting in an iterative setting is guaranteed to converge to an equilibrium. An alternative behavior is analyzed, where voters try to minimize their worst-case regret. As it turns out, the two behaviors coincide in the simple setting of Meir et al.[EC'14], but not in the general case.

This paper proposes a mapping between multi-agent pathfinding (MAPF) and combinatorial auctions (CAs). In MAPF, agents need to reach their goal destinations without colliding. Algorithms for solving MAPF aim at assigning agents non-conflicting paths that minimize agents' travel costs. In CA problems, agents bid over bundles of items they desire. Auction mechanisms aim at finding an allocation of bundles that maximizes social welfare. In the proposed mapping of MAPF to CAs, agents bid on paths to their goals and the auction allocates non-colliding paths to the agents. Using this formulation, auction mechanisms can be naturally used to solve a range of MAPF problem variants. In particular, auction mechanisms can be applied to non-cooperative settings with self-interested agents while providing optimality guarantees and robustness to manipulations by agents. The paper further shows how to efficiently implement an auction mechanism for MAPF, utilizing methods and representations from both the MAPF and CA literatures.

We study the problem of eliciting and aggregating probabilistic information from multiple agents. In order to successfully aggregate the predictions of agents, the principal needs to elicit some notion of confidence from agents, capturing how much experience or knowledge led to their predictions. To formalize this, we consider a principal who wishes to learn the distribution of a random variable. A group of Bayesian agents has each privately observed some independent samples of the random variable. The principal wishes to elicit enough information from each agent, so that her posterior is the same as if she had directly received all of the samples herself. Leveraging techniques from Bayesian statistics, we represent confidence as the number of samples an agent has observed, which is quantified by a hyperparameter from a conjugate family of prior distributions. This then allows us to show that if the principal has access to a few samples, she can achieve her aggregation goal by eliciting predictions from agents using proper scoring rules. In particular, with access to one sample, she can successfully aggregate the agents' predictions if and only if every posterior predictive distribution corresponds to a unique value of the hyperparameter, a property which holds for many common distributions of interest. When this uniqueness property does not hold, we construct a novel and intuitive mechanism where a principal with two samples can elicit and optimally aggregate the agents' predictions.

Motivated by current day crowdsourcing platforms and emergence of online labor markets, this work addresses the problem of task allocation and payment decisions when unreliable and strategic workers arrive over time to work on tasks which must be completed within a deadline. We consider the following scenario: a requester has a set of tasks that must be completed before a deadline; agents (aka crowd workers) arrive over time and it is required to make sequential decisions regarding task allocation and pricing. Agents may have different costs for providing service and these costs are private information of the agents. We assume that agents are not strategic about their arrival times but could be strategic about their costs of service. In addition, agents could be unreliable in the sense of not being able to complete the assigned tasks within the allocated time; these tasks must then be reallocated to other agents to ensure ontime completion of the set of tasks by the deadline. For this setting, we propose two mechanisms: a DPM (DynamicPrice Mechanism) and an ABM (Auction Based Mechanism). Both mechanisms are dominant strategy incentive compatible, budget feasible, and also satisfy ex-post individual rationality for agents who complete the allocated tasks. These mechanisms can be implemented in current day crowdsourcing platforms with minimal changes to the current interaction model.

We put forward a new model of congestion games where agents have uncertainty over the routes used by other agents. We take a non-probabilistic approach, assuming that each agent knows that the number of agents using an edge is within a certain range. Given this uncertainty, we model agents who either minimize their worst-case cost (WCC) or their worst-case regret (WCR), and study implications on equilibrium existence, convergence through adaptive play, and efficiency. Under the WCC behavior the game reduces to a modified congestion game, and welfare improves when agents have moderate uncertainty. Under WCR behavior the game is not, in general, a congestion game, but we show convergence and efficiency bounds for a simple class of games.

In a search task, a group of agents compete to be the first to find the solution. Each agent has different private information to incorporate into its search. This problem is inspired by settings such as scientific research, Bitcoin hash inversion, or hunting for some buried treasure. A social planner such as a funding agency, mining pool, or pirate captain might like to convince the agents to collaborate, share their information, and greatly reduce the cost of searching. However, this cooperation is in tension with the individuals' competitive desire to each be the first to win the search. The planner's proposal should incentivize truthful information sharing, reduce the total cost of searching, and satisfy fairness properties that preserve the spirit of the competition. We design contract-based mechanisms for information sharing without money. The planner solicits the agents' information and assigns search locations to the agents, who may then search only within their assignments. Truthful reporting of information to the mechanism maximizes an agent's chance to win the search. Epsilon-voluntary participation is satisfied for large search spaces. In order to formalize the planner's goals of fairness and reduced search cost, we propose a simplified, simulated game as a benchmark and quantify fairness and search cost relative to this benchmark scenario. The game is also used to implement our mechanisms. Finally, we extend to the case where coalitions of agents may participate in the mechanism, forming larger coalitions recursively.

Children with complex health conditions require care from a large, diverse set of caregivers that includes parents and community support organizations as well as multiple types of medical professionals. Coordination of their care is essential for good outcomes, and  extensive  research has shown that the use of integrated, team-based care plans improves care coordination. Care plans, however, are rarely deployed in practice. This paper describes barriers to effective implementation of care plans in complex care revealed by a study of care providers treating such children. It draws on teamwork theories, identifying ways AI capabilities could enhance care plan use; describes the design of GoalKeeper, a system to support providers use of care plans; and describes  initial work toward information sharing algorithms for such systems.

Welcome to the Second AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2014) held November 2-4, 2014, in Pittsburgh, Pennsylvania. This conference is an opportunity to build on the success of the First AAAI Human Computation and Crowdsourcing conference, and to promote the best scholarship in this vibrant and fast emerging, multidisciplinary area. The conference also comes on the heels of four HCOMP workshops, including two workshops hosted at the annual AAAI conference. The HCOMP conference is designed to be a venue for exchanging ideas and developments on principles, experiments, and implementations of systems that rely on programmatic access to human intellect to perform some aspect of computation, or where human perception, knowledge, reasoning, or coordinated activity contributes to the operation of larger systems and applications. Topics relevant to the discipline of human computation and crowdsourcing include human-computer interaction (HCI), computer-supported collaborative work (CSCW), cognitive psychology, organizational behavior, economics, information retrieval, databases, computer systems and programming languages, and optimization.

With a large amount of tasks of various types, requesters in crowdsourcing platforms often bundle tasks of different types into a single working session. This creates a task switching setting, where workers need to shift between different cognitive tasks. We design and conduct an experiment on Amazon Mechanical Turk to study how occasionally presented performance-contingent monetary rewards, referred as monetary interventions , affect worker performance in the task switching setting. We use two competing metrics to evaluate worker performance. When monetary interventions are placed on some tasks in a working session, our results show that worker performance on these tasks can be improved in both metrics. Moreover, worker performance on other tasks where monetary interventions are not placed is also affected: workers perform better according to one metric, but worse according to the other metric. This suggests that in addition to providing extrinsic monetary incentives for some tasks, monetary interventions implicitly set performance goals for all tasks. Furthermore, monetary interventions are most effective in improving worker performance when used at switch tasks, tasks that follow a task of a different type, in working sessions with a low task switching frequency.

This workshop aims to bring together a wide range of computer scientists, biomedical and health informaticians, researchers, students, industry professionals, national and international public health agencies, and NGOs interested in the theory and practice of computational models of web-based public health intelligence to highlight the latest achievements in epidemiological surveillance based on monitoring online communications and interactions on the World Wide Web. The workshop includes contributions on theory, methods, systems, and applications of data mining, machine learning, databases, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based health-care applications, with focus on public health.