Author name cluster
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
AAMAS Conference 2025 Conference Paper
In this work we present the idea of partitioning contestants into disjoint groups, each competing in an independent contest, with its own prize. We focus on binary contests, wherein contestants choose whether or not to participate, and show that such contest partitioning can benefit the organizer running the contest when partitioning entails a cost.
JAAMAS Journal 2024 Journal Article
Abstract In this work we explore the opportunities presented by partitioning contestants in contest into disjoint groups, each competing in an independent contest, with its own prize. This, as opposed to most literature on contest design, which focuses on the setting of a single “grand” (possibly multi-stage) contest, wherein all potential contestants ultimately compete for the same prize(s), with few exceptions that do consider contest partitioning, yet with conflicting preference results concerning the optimal structure to be used. Focusing on binary contests, wherein the quality of contestants’ submissions are endogenously determined, we show that contest partitioning is indeed beneficial under some condition, e. g. , whenever the number of contestants, or the prize amount, are “sufficiently large”, where the exact size requirements are a function of the partitioning cost. When partitioning does not entail any cost, we show that it is either a dominating or weakly dominating strategy, depending on the way the organizer’s expected benefit is determined. The analysis is further extended to consider partitioning where some of the sub-contests used contain a single contestant (a singleton). We conclude that contest partitioning is an avenue that contest designers can and should consider, when aiming to maximize their profit.
EUMAS Conference 2021 Conference Paper
Abstract Norms and conventions enable coordination in populations of agents by establishing patterns of behaviour, which can emerge as agents interact with their environment and each other. Previous research on norm emergence typically considers pairwise interactions, where agents’ rewards are endogenously determined. In many real-life domains, however, individuals do not interact with one other directly, but with their environment, and the resources associated with actions are often congested. Thus, agents’ rewards are exogenously determined as a function of others’ actions and the environment. In this paper, we propose a framework to represent this setting by: (i) introducing congested actions; and (ii) adding a central authority, that is able to manipulate agents’ rewards. Agents are heterogeneous in terms of their reward functions, and learn over time, enabling norms to emerge. We illustrate the framework using transport modality choice as a simple scenario, and investigate the effect of representative manipulations on the emergent norms.
AAMAS Conference 2019 Conference Paper
AAMAS Conference 2019 Conference Paper
IJCAI Conference 2019 Conference Paper
We study temporal information design in contests, wherein the organizer may, possibly incrementally, disclose information about the participation and performance of some contestants to other (later) contestants. We show that such incremental disclosure can increase the organizer's profit. The expected profit, however, depends on the exact information disclosure structure, and the optimal structure depends on the parameters of the problem. We provide a game-theoretic analysis of such information disclosure schemes as they apply to two common models of contests: (a) simple contests, wherein contestants' decisions concern only their participation; and (b) Tullock contests, wherein contestants choose the effort levels to expend. For each of these we analyze and characterize the equilibrium strategy, and exhibit the potential benefits of information design.
IJCAI Conference 2018 Conference Paper
Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e. g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a simple contest, wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the revised mechanism the principal can not only force her most desired pure-strategies based equilibrium to emerge, but also, at times, end up with an equilibrium offering a greater expected profit. Further, we show that in the modified contest the optimal prize can be effectively computed. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal, both in terms of an increased over-participation in the contest (compared to theoretical expectations) and increased average profit.
AAAI Conference 2018 Conference Paper
One key motivation for using contests in real-life is the substantial evidence reported in empirical contest-design literature for people’s tendency to act more competitively in contests than predicted by the Nash Equilibrium. This phenomenon has been traditionally explained by people’s eagerness to win and maximize their relative (rather than absolute) payoffs. In this paper we make use of “simple contests”, where contestants only need to strategize on whether to participate in the contest or not, as an infrastructure for studying whether indeed more effort is exerted in contests due to competitiveness, or perhaps this can be attributed to other factors that hold also in non-competitive settings. The experimental methodology we use compares contestants’ participation decisions in eight contest settings differing in the nature of the contest used, the number of contestants used and the theoretical participation predictions to those obtained (whenever applicable) by subjects facing equivalent non-competitive decision situations in the form of a lottery. We show that indeed people tend to over-participate in contests compared to the theoretical predictions, yet the same phenomenon holds (to a similar extent) also in the equivalent non-competitive settings. Meaning that many of the contests used nowadays as a means for inducing extra human effort, that are often complex to organize and manage, can be replaced by a simpler non-competitive mechanism that uses probabilistic prizes.
IJCAI Conference 2017 Conference Paper
This paper studies the problem of designing contests for settings where a principal seeks to optimize the quality of the best performance obtained, and potential contestants only strategize about whether to participate in the contest, as participation incurs some cost. This type of contest can be mapped to various real-life settings (e. g. , an audition, a beauty pageant, technology crowdsourcing). The paper provides a comparative game-theoretic based solution to two variants of the above underlying model: parallel and sequential contest, enabling a characterization of the equilibrium strategies in each. Special emphasis is placed on the case where the contestants are homogeneous which is often the case in real-life whenever the contestants are basically alike and their ranking in the contest is mostly influenced by some probabilistic factors (e. g. , luck). Here, several (somehow counter-intuitive) properties of the equilibrium are proved, in particular for the sequential contest, leading to a comprehensive characterization of the principal preference between the two.
AAAI Conference 2016 Conference Paper
This paper studies two suboptimal advice provisioning methods (“advisors”) as an alternative to providing optimal advice in repeated advising settings. Providing users with suboptimal advice has been reported to be highly advantageous whenever the optimal advice is non-intuitive, hence might not be accepted by the user. Alas, prior methods that rely on suboptimal advice generation were designed primarily for a single-shot advice provisioning setting, hence their performance in repeated settings is questionable. Our methods, on the other hand, are tailored to the repeated interaction case. Comprehensive evaluation of the proposed methods, involving hundreds of human participants, reveals that both methods meet their primary design goal (either an increased user profit or an increased user satisfaction from the advisor), while performing at least as good with the alternative goal, compared to having people perform with: (a) no advisor at all; (b) an advisor providing the theoretic-optimal advice; and (c) an effective suboptimal-advice-based advisor designed for the non-repeated variant of our experimental framework.