Sigal Oren

Mechanisms for (mis)allocating scientific credit

Scientific communities confer many forms of credit --- both implicit and explicit --- on their successful members, and it has long been argued that the motivation provided by these forms of credit helps to shape a communitys collective attention toward different lines of research. The allocation of scientific credit, however, has also been the focus of long-documented pathologies: certain research questions are said to command too much credit, at the expense of other equally important questions; and certain researchers (in a version of Robert Mertons Matthew Effect) seem to receive a disproportionate share of the credit, even when the contributions of others are similar. Here we show that the presence of each of these pathologies can in fact increase the collective productivity of a community. We consider a model for the allocation of credit, in which individuals can choose among projects of varying levels of importance and difficulty, and they compete to receive credit with others who choose the same project. Under the most natural mechanism for allocating credit, in which it is divided among those who succeed at a project in proportion to the projects importance, the resulting selection of projects by self-interested, credit-maximizing individuals will in general be socially sub-optimal. However, we show that there exist ways of allocating credit out of proportion to the true importance of the projects, as well as mechanisms that assign credit out of proportion to the relative contributions of the individuals, that lead credit-maximizing individuals to collectively achieve social optimality. These results therefore suggest how well-known forms of misallocation of scientific credit can in fact serve to channel self-interested behavior into socially optimal outcomes.

Economic efficiency requires interaction

We study the necessity of interaction between individuals for obtaining approximately efficient economic allocations. We view this as a formalization of Hayeks classic point of view that focuses on the information transfer advantages that markets have relative to centralized planning. We study two settings: combinatorial auctions with unit demand bidders (bipartite matching) and combinatorial auctions with subadditive bidders. In both settings we prove that non-interactive protocols require exponentially larger communication costs than interactive ones, even those that only use a modest amount of interaction.

Optimization with demand oracles

We study combinatorial procurement auctions, where a buyer with a valuation function v and budget B wishes to buy a set of items. Each item i has a cost ci and the buyer is interested in a set S that maximizes v(S) subject to ∑i∈Sci ≤ β. Special cases of combinatorial procurement auctions are well-studied problems from submodular optimization. In particular, when the costs are all equal (cardinality constraint), a classic result by Nemhauser et al shows that the greedy algorithm provides an e/e-1 approximation. Motivated by many papers that utilize demand queries to elicit the preferences of agents in economic settings, we develop algorithms that guarantee improved approximation ratios in the presence of demand oracles. We are able to break the e/e-1 barrier: we present algorithms that use only polynomially many demand queries and have approximation ratios of 9/8+∈ for the general problem and 9/8 for maximization subject to a cardinality constraint. We also consider the more general class of subadditive valuations. We present algorithms that obtain an approximation ratio of 2+∈ for the general problem and 2 for maximization subject to a cardinality constraint. We guarantee these approximation ratios even when the valuations are non-monotone. We show that these ratios are essentially optimal, in the sense that for any constant ∈>0, obtaining an approximation ratio of 2-∈ requires exponentially many demand queries.

On Bitcoin and red balloons

In this letter we present a brief report of our recent research on information distribution mechanisms in networks [Babaioff et al. 2011]. We study scenarios in which all nodes that become aware of the information compete for the same prize, and thus have an incentive not to propagate information. Examples of such scenarios include the 2009 DARPA Network Challenge (finding red balloons), and raffles. We give special attention to one application domain, namely Bitcoin, a decentralized electronic currency system. We propose reward schemes that will remedy an incentives problem in Bitcoin in a Sybil-proof manner, with little payment overhead.

Planning with Multiple Biases

Recent work has considered theoretical models for the behavior of agents with specific behavioral biases: rather than making decisions that optimize a given payoff function, the agent behaves inefficiently because its decisions suffer from an underlying bias. These approaches have generally considered an agent who experiences a single behavioral bias, studying the effect of this bias on the outcome. In general, however, decision-making can and will be affected by multiple biases operating at the same time. How do multiple biases interact to produce the overall outcome? Here we consider decisions in the presence of a pair of biases exhibiting an intuitively natural interaction: present bias -- the tendency to value costs incurred in the present too highly -- and sunk-cost bias -- the tendency to incorporate costs experienced in the past into ones plans for the future. We propose a theoretical model for planning with this pair of biases, and we show how certain natural behavioral phenomena can arise in our model only when agents exhibit both biases. As part of our model we differentiate between agents that are aware of their biases (sophisticated) and agents that are unaware of them (naive). Interestingly, we show that the interaction between the two biases is quite complex: in some cases, they mitigate each others effects while in other cases they might amplify each other. We obtain a number of further results as well, including the fact that the planning problem in our model for an agent experiencing and aware of both biases is computationally hard in general, though tractable under more relaxed assumptions.

The complexity of social coordination

Coordination is a challenging everyday task; just think of the last time you organized a party or a meeting involving several people. As a growing part of our social and professional life goes online, an opportunity for an improved coordination process arises. Recently, Gupta et al. proposed entangled queries as a declarative abstraction for data-driven coordination, where the difficulty of the coordination task is shifted from the user to the database. Unfortunately, evaluating entangled queries is very hard, and thus previous work considered only a restricted class of queries that satisfy safety (the coordination partners are fixed) and uniqueness (all queries need to be satisfied). In this paper we significantly extend the class of feasible entangled queries beyond uniqueness and safety. First, we show that we can simply drop uniqueness and still efficiently evaluate a set of safe entangled queries. Second, we show that as long as all users coordinate on the same set of attributes, we can give an efficient algorithm for coordination even if the set of queries does not satisfy safety. In an experimental evaluation we show that our algorithms are feasible for a wide spectrum of coordination scenarios.

Dynamic Models of Reputation and Competition in Job-Market Matching

A fundamental decision faced by a firm hiring employees --- and a familiar one to anyone who has dealt with the academic job market, for example --- is deciding what caliber of candidates to pursue. Should the firm try to increase its reputation by making offers to higher-quality candidates, despite the risk that the candidates might reject the offers and leave the firm empty-handed? Or is it better to play it safe and go for weaker candidates who are more likely to accept the offer? The question acquires an added level of complexity once we take into account the effect one hiring cycle has on the next: hiring better employees in the current cycle increases the firms reputation, which in turn increases its attractiveness for higher-quality candidates in the next hiring cycle. These considerations introduce an interesting temporal dynamic aspect to the rich line of research on matching models for job markets, in which long-range planning and evolving reputational effects enter into the strategic decisions made by competing firms. The full set of ingredients in such recruiting decisions is complex, and this has made it difficult to model the fundamental strategic tension at the core of the problem. Here we develop a model based on two competing firms to try capturing as cleanly as possible the elements that we believe constitute this strategic tension: the trade-off between short-term recruiting success and long-range reputation-building; the inefficiency that results from underemployment of people who are not ranked highest; and the influence of earlier accidental outcomes on long-term reputations. Our model exhibits all these phenomena in a stylized setting, governed by a parameter

Selection and influence in cultural dynamics

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Side-communication yields efficiency of ascending auctions: The two-items case

that captures the difference in strength between the top candidate in each hiring cycle and the next best. Building on an economic model of competition between parties of unequal strength, we show that when

Dynamics of Evolving Social Groups

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