Unfair rating attacks happen in existing trust and reputation systems, lowering the quality of the systems. There exists a formal model that measures the maximum impact of independent attackers [Wang et al., 2015] — based on information theory. We improve on these results in multiple ways: (1) we alter the methodology to be able to reason about colluding attackers as well, and (2) we extend the method to be able to measure the strength of any attacks (rather than just the strongest attack). Using (1), we identify the strongest collusion attacks, helping construct robust trust system. Using (2), we identify the strength of (classes of) attacks that we found in the literature. Based on this, we help to overcome a shortcoming of current research into collusion-resistance — specific (types of) attacks are used in simulations, disallowing direct comparisons between analyses of systems.

Positional scoring rules are often used for rank aggregation. In this work we study how scoring rules can be formulated as the minimization of some distance measures between rankings, and we also consider a new family of aggregation methods, called biased scoring rules. This work extends a previous known observation connecting Borda count with the minimization of the sum of the Spearman distances (calculated with respect to a set of input rankings). In particular we consider generalizations of the Spearman distance that can give different weights to items and positions; we also handle the case of incomplete rank data. This has applications in the clustering of rank data, where two main steps need to be performed: aggregating rankings of the same cluster into a representative ranking (the cluster's centroid) and assigning each ranking to its closest centroid. Using the proper combination of scoring rules (for aggregation) and distances (for assignment), it is possible to perform clustering in a computationally efficient way and as well account for specific desired behaviors (give more weight to top positions, bias the centroids in favor of particular items).

Pricing-based mechanisms have been widely studied and developed for resource allocation in multi-agent systems. One of the main goals in such studies is to avoid envy between the agents, i.e., guarantee fair allocation. However, even the simplest combinatorial cases of this problem is not well understood. Here, we try to fill these gaps and design polynomial revenue maximizing pricing mechanisms to allocate homogeneous resources among buyers in envy-free manner. In particular, we consider envy-free outcomes in which all buyers' utilities are maximized. We also consider pair envy-free outcomes in which all buyers prefer their allocations to the allocations obtained by other agents. For both notions of envy-freeness, we consider item and bundle pricing schemes. Our results clearly demonstrate the limitations and advantages in terms of revenue between these two different notions of envy-freeness.

We consider three important, non-strategyproof assignment mechanisms: Probabilistic Serial and two variants of the Boston mechanism. Under each of these mechanisms, we study the agent’s manipulation problem of determining a best response, i.e., a report that maximizes the agent’s expected utility. In particular, we consider local manipulation strategies, which are simple heuristics based on local, greedy search. We make three main contributions. First, we present results from a behavioral experiment (conducted on Amazon Mechanical Turk) which demonstrate that human manipulation strategies can largely be explained by local manipulation strategies. Second, we prove that local manipulation strategies may fail to solve the manipulation problem optimally. Third, we show via large-scale simulations that despite this non-optimality, these strategies are very effective on average. Our results demonstrate that while the manipulation problem may be hard in general, even cognitively or computationally bounded (human) agents can find near-optimal solutions almost all the time via simple local search strategies.

Animals in social foraging not only present the ordered and aggregated group movement but also the individual movement patterns of Lévy walks that are characterized as the power-law frequency distribution of flight lengths. The environment and the conspecific effects between group members are two fundamental inducements to the collective behavior. However, most previous models emphasize one of the two inducements probably because of the great difficulty to solve the behavior conflict caused by two inducements. Here, we propose an environment-driven social force model to simulate overall foraging process of an agent group. The social force concept is adopted to quantify the conspecific effects and the interactions between individuals and the environment. The cohesion-first rule is implemented to solve the conflict, which means that individuals preferentially guarantee the collective cohesion under the environmental effect. The obtained results efficiently comply with the empirical reports that mean the Lévy walk pattern of individual movement paths and the high consistency and cohesion of the entity group. By extensive simulations, we also validate the impact of two inducements for individual behaviors in comparison with several classic models

Two incentive mechanisms for Boolean games were proposed recently - taxation schemes and side payments. Both mechanisms have been shown to be able to secure a pure Nash equilibrium (PNE) for Boolean games. A complete characterization of outcomes that can be transformed to PNEs is given for each of the two incentive mechanisms. Side payments are proved to be a weaker mechanism in the sense that the outcomes that they can transform to PNEs are a subset of those transformable by taxation. A family of social-network-based Boolean games, which demonstrates the differences between the two mechanisms for securing a PNE, is presented. A distributed search algorithm for finding the side payments needed for securing a PNE is proposed. An empirical evaluation demonstrates the properties of the two mechanisms on the family of social-network-based Boolean games.

In the contemporary autonomous systems the role of complex interactions such as (possibly relaxed) dialogues is increasing significantly. In this paper we provide a paraconsistent and paracomplete implementation of inquiry dialogue under realistic assumptions regarding availability and quality of information. Various strategies for dealing with unsure and inconsistent information are analyzed. The corresponding dialogue outcomes are further evaluated against the (paraconsistent and paracomplete) distributed beliefs of the group. A specific 4-valued logic underpins the presented framework. Thanks to the qualities of the implementation tool: a rule-based query language 4QL, our solution is both expressive and tractable.

The intractability in cooperative, decentralized control models is mainly due to prohibitive memory requirements in both optimal policies and value functions. The complexity analysis has emerged as the standard method to estimating the memory needed for solving a given computational problem, but complexity results may be somewhat limited. This paper introduces a general methodology — structural analysis — for the design of optimality-preserving concise policies and value functions, which will eventually lead to the development of efficient theory and algorithms. For the first time, we show that memory requirements for policies and value functions may be asymmetric, resulting in cooperative, decentralized control models with exponential reductions in memory requirements.

Demange (2012) generalized the classical single-crossing property to the intermediate property on median graphs and proved that the representative voter theorem still holds for this more general framework. We complement her result with proving that the linear orders of any profile which is intermediate on a median graph  form a Condorcet domain. We prove that for any median graph there exists a profile that is intermediate with respect to that graph and that one may need at least as many alternatives as vertices to construct such a profile.  We provide a polynomial-time algorithm to recognize whether or not a given profile is intermediate with respect to some median graph. Finally, we show that finding winners for the Chamberlin-Courant rule is polynomial-time solvable  for profiles that are single-crossing on a tree.

Voting rules are powerful tools that allow multiple agents to aggregate their preferences in order to reach joint decisions. A common flaw of some voting rules, known as the no-show paradox, is that agents may obtain a more preferred outcome by abstaining from an election. We study strategic abstention for set-valued voting rules based on Kelly's and Fishburn's preference extensions. Our contribution is twofold. First, we show that, whenever there are at least five alternatives, every Pareto-optimal majoritarian voting rule suffers from the no-show paradox with respect to Fishburn's extension. This is achieved by reducing the statement to a finite---yet very large---problem, which is encoded as a formula in propositional logic and then shown to be unsatisfiable by a SAT solver. We also provide a human-readable proof which we extracted from a minimal unsatisfiable core of the formula. Secondly, we prove that every voting rule that satisfies two natural conditions cannot be manipulated by strategic abstention with respect to Kelly's extension. We conclude by giving examples of well-known Pareto-optimal majoritarian voting rules that meet these requirements.