Abstract In this study, we explore the concept of anchoring bias in the context of sequential sentiment analysis of review corpora. We introduce a novel approach that involves using a carefully selected, limited group of reviews at the beginning of the annotation process for calibration, aiming to reduce this bias. Through an extensive set of experiments we confirm the existence of sentiment bias and demonstrate that indeed its impact can be moderated through initial calibration. We also demonstrate that the composition of the calibration set is critical, underscoring the importance of establishing sound criteria for selecting these initial reviews. By comparing the accuracy of annotators who utilized our calibration method against those who did not calibrate or used a randomly chosen set for calibration, we found that our method significantly decreases the overall annotation error. Moreover, the guidelines we develop for selecting the calibration set prove to be highly effective and adaptable, even when applied to a domain other than the one they were originally developed for. Acknowledging the overhead of labeling calibration reviews, we demonstrate that this approach is more efficient compared to eliciting multiple sentiment scores per review, a common strategy to reduce Mean Absolute Error (MAE). Our findings reveal that the proposed calibration method significantly reduces resource expenditure, compared to relying on parallel labeling, while maintaining accuracy.

This paper focuses in the inherent anchoring bias present in sequential reviews-sentiment corpora annotation processes. It proposes employing a limited subset of meticulously chosen reviews at the outset of the process, as a means of calibration, effectively mitigating the phenomenon. Through extensive experimentation we validate the phenomenon of sentiment bias in the annotation process and show that its magnitude can be influenced by pre-calibration. Furthermore, we show that the choice of the calibration set matters, hence the need for effective guidelines for choosing the reviews to be included in it. A comparison of annotators performance with the proposed calibration to annotation processes that do not use calibration or use a randomly-picked calibration set, reveals that indeed the calibration set picked is highly effective---it manages to substantially reduce the average absolute error compared to the other cases. Furthermore, the proposed selection guidelines are found to be highly robust in picking an effective calibration set also for domains different than the one based on which these rules were extracted.

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.

In recent years, autonomous agents have been increasingly handling decision tasks on behalf of their human users. One such type of task with much potential to be carried out by an assisting autonomous agent is optimal stopping (e.g., in costly search). In such case, when it is the agent’s responsibility to decide when to terminate search, the challenge of maximizing user satisfaction with the process becomes acute. This paper provides evidence for the loose correlation between agent performance, profit-wise, and user satisfaction in this application domain, ruling out the use of the profit-maximizing strategy. As an alternative, it proposes a strategy relying on behavioral features. An extensive comparative evaluation of the proposed strategy, as well as the profit-maximizing strategy and the highest ranked strategy elicited through crowdsourcing reveals that the average satisfaction with the first is substantially greater than when experiencing with the others. The analysis of the results also reveals several important insights related to people’s ability to estimate the effectiveness of search strategies, satisfaction-wise, or propose such strategies themselves, contributing to the study of how human beings deal with optimal stopping problems in practice.

In various human-agent collaborative settings the agent is fault-prone. In many of these settings, it is possible that the human user will also account to failure, hindering task execution. In this paper we study the effect of the latter type of failures over the user’s satisfaction with the agent in the collaborative setting. We report the results of two experiments, differing in the number of agent’s faults and the way faults influence task progress and attract players’ focus, with 264 subjects recruited and interacted through Amazon Mechanical Turk. We find that when the user accounts for some faults during the collaborative execution of the task, she becomes more forgiving to the agent faults, and consequently more satisfied with the collaboration, compared to the case where she makes no faults. The importance of this finding becomes most apparent in the design of collaborative agents.

In many settings where an agent needs to suggest or recommend a course of action to its user, the agent’s goal may not fully align with the user’s goal. In particular, the agent may maximize its benefit if the user chooses specific alternatives that are not necessarily the ones that maximize her own individual benefit. In this paper we study such setting in the context of providing advice in two-armed bandit problems. We explore a potential strategy for the agent aiming to influence the arm to be picked. In particular we focus on a somehow naive recommendation strategy that always recommend the preferred arm and a strategy that recommends based on various Machine Learning models that aim to guide the decision regarding when to switch to the agent’s least preferred arm. Based on extensive evaluation we find that both recommendation strategies results in better performance compared to not making any recommendation, and that the naive recommendation strategy performs slightly better than the ML-based recommendations, despite using a substantial amount of training data for the latter.

This paper provides and evaluates a new paradigm for collaborative human-robot operation in search and rescue-like settings with information asymmetry. In particular, we focus on settings where the human, a crowdworker in our case, is used as a sensor, providing the route-planning module with essential environmental information. In such settings, the ability to predict the expected performance of the collaborating crowdworker in real-time is instrumental for maintaining a continuously high level of performance. Through an extensive set of experiments with crowdworkers recruited and interacted through Amazon Mechanical Turk, we show that effective online prediction is indeed possible, however only if distinguishing between two subpopulations of crowdworkers, termed ”operators” and ”sensors”, applying a different prediction model to each. Furthermore, we show that even the classification of crowdworkers to the two types can be carried out successfully in real-time, based merely on the first two minutes of collaboration. Finally, we demonstrate how the above abilities can be used for a more effective workers’ recruiting process, resulting in a substantially improved overall performance.

This paper proposes a heuristic algorithm for effectively summarizing the work of novice robot operators, e.g., ones recruited through crowdsourcing platforms, in search and rescue-like tasks. Such summaries can be used for many purposes, perhaps most notably for monitoring and evaluating an operator’s performance in settings where information gaps preclude automatic evaluation. The underlying idea of our method is dividing the task timeline into intervals, and extracting a subset of high-scoring and low-scoring segments within, using a heuristic scoring function. This results in a short effective summary of the operator’s work, based on which several other crowdworkers can evaluate her performance. The effectiveness of the proposed method was extensively evaluated and compared to a large set of alternative methods through a series of experiments in Amazon Mechanical Turk. The analysis of the results reveals that the proposed method outperforms all tested alternatives. Finally, we evaluate the performance one may achieve with the use of machine learning for predicting the operator’s performance in our domain. While this approach manages to reach a performance level similar to the one achieved with summaries, it requires an order-of-magnitude greater effort for training (measured in terms of crowdworkers time).

This paper suggests a new paradigm for the design of collaborative autonomous agents engaged in executing a joint task alongside a human user. In particular, we focus on the way an agent's failures should affect its decision making, as far as user satisfaction measures are concerned. Unlike the common practice that considers agent (and more broadly, system) failures solely in the prism of their influence over the agent's contribution to the execution of the joint task, we argue that there is an additional, direct, influence which cannot be fully captured by the above measure. Through two series of large-scale controlled experiments with 450 human subjects, recruited through Amazon Mechanical Turk, we show that, indeed, such direct influence holds. Furthermore, we show that the use of a simple agent design that takes into account the direct influence of failures in its decision making yields considerably better user satisfaction, compared to an agent that focuses exclusively on maximizing its absolute contribution to the joint task.

In many approval voting settings voters are a priori uncertain regarding their true preferences, yet can obtain this information if willing to incur some cost. This paper provides a comprehensive analysis of such model focusing in simultaneous and sequential voting. The analysis enables demonstrating that costly preference-related information acquisition changes some inherent model properties. In particular, the introduction of such cost may lead to all sorts of manipulations in the sequential case, resulting in an assortment of examples where the latter is dominated by simultaneous voting and vice versa. This, as opposed to the case where such information is freely available, where it can be proved that the two variants are truthful and equivalent. These findings suggest important implications to policy makers and the designers of voting systems.

The recent massive proliferation of affiliate marketing suggests a new e-commerce paradigm which involves sellers, affiliates and the platforms that connect them. In particular, the fact that prospective buyers may become acquainted with the promotion through more than one affiliate to whom they are connected calls for new mechanisms for compensating affiliates for their promotional efforts. In this paper, we study the problem of a platform that needs to decide on the commission to be awarded to affiliates for promoting a given product or service. Our equilibrium-based analysis, which applies to the case where affiliates are a priori homogeneous and self-interested, enables showing that a minor change in the way the platform discloses information to the affiliates results in a tremendous (positive) effect on the platform's expected profit. In particular, we show that with the revised mechanism the platform can overcome the multi-equilibria problem that arises in the traditional mechanism and can obtain a profit which is at least as high as the maximum profit in any of the equilibria that hold in the latter.

This paper studies the benefit in using signaling by an information seller holding information that can completely disambiguate some uncertainty concerning the state of the world for the information buyer. We show that a necessary condition for having the information seller benefit from signaling in this model is having some ``seed of truth" in the signaling scheme used. We then introduce two natural signaling mechanisms that adhere to this condition, one where the seller pre-commits to the signaling scheme to be used and the other where she commits to use a signaling scheme that contains a ``seed of truth". Finally, we analyze the equilibrium resulting from each and show that, somehow counter-intuitively, despite the inherent differences between the two mechanisms, they are equivalent in the sense that for any equilibrium associated with the maximum revenue in one there is an equilibrium offering the seller the same revenue in the other.

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.

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.

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.

This paper studies the benefit for information providers in free public information disclosure in settings where the prospective information buyers are people. The underlying model, which applies to numerous real-life situations, considers a standard decision making setting where the decision maker is uncertain about the outcomes of her decision. The information provider can fully disambiguate this uncertainty and wish to maximize her profit from selling such information. We use a series of AMT-based experiments with people to test the benefit for the information provider from reducing some of the uncertainty associated with the decision maker's problem, for free. Free information disclosure of this kind can be proved to be ineffective when the buyer is a fully-rational agent. Yet, when it comes to people we manage to demonstrate that a substantial improvement in the information provider's profit can be achieved with such an approach. The analysis of the results reveals that the primary reason for this phenomena is people's failure to consider the strategic nature of the interaction with the information provider. Peoples' inability to properly calculate the value of information is found to be secondary in its influence.

With the increasing interest in the role information providers play in multi-agent systems, much effort has been dedicated to analyzing strategic information disclosure and signaling by such agents. This paper analyzes the problem in the context of auctions (specifically for second-price auctions). It provides an equilibrium analysis to the case where the information provider can use signaling according to some pre-committed scheme before introducing its regular (costly) information selling offering. The signal provided, publicly discloses (for free) some of the information held by the information provider. Providing the signaling is thus somehow counter intuitive as the information provider ultimately attempts to maximize her gain from selling the information she holds. Still, we show that such signaling capability can be highly beneficial for the information provider and even improve social welfare. Furthermore, the examples provided demonstrate various possible other beneficial behaviors available to the different players as well as to a market designer, such as paying the information provider to leave the system or commit to a specific signaling scheme. Finally, the paper provides an extension of the underlying model, related to the use of mixed signaling strategies.

This paper studies the use of a multi-prize compensation scheme for "simple" contests where participation is costly and the quality of participants' contributions is a priori uncertain at the time they make their decision related to participating in the contest. The equilibrium analysis provided enables demonstrating not only that a multi-prize structure is often beneficial but also that in some cases the principal's expected profit is maximized when offering a second prize greater than the first prize. This may seem somehow counter-intuitive especially given that the principal's profit is only influenced by the quality of the best submission rather than the aggregate of submissions. Special emphasis is placed on the case where the contestants are a priori homogeneous which is often the case in real-life, whenever the contestants are basically a priori alike and the quality of their submissions is determined subjectively by some referee. Here, we manage to prove that a multi-prize structure is dominated by a winner-takes-all scheme, suggesting that the benefit in the multi-prize contest scheme fully derives from the heterogeneity between prospective contestants. Finally, we show that there is a class of settings where the use of the multi-prize crowdsourcing contest model enables achieving the performance of the fully cooperative model (which is an upper bound for the performance in any type of contest), and that for settings of this class the optimal prize allocation can be extracted through a set of linear equations.

This paper presents methods for improving the attention span of workers in tasks that heavily rely on their attention to the occurrence of rare events. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. The proposed approach is an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly.

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.

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.

This paper studies a new paradigm for improving the attention span of workers in tasks that heavily rely on user's attention to the occurrence of rare events. Such tasks are highly common, ranging from crime monitoring to controlling autonomous complex machines, and many of them are ideal for crowdsourcing. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. This, as an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly. We use extensive experimentation to compare the methods with the traditional approach of inducing attention through rewarding the identification of the event of interest and within the three. The analysis of the results indicates that with the use of dummy events a substantially more favorable tradeoff between the detection (of the event of interest) probability and the expected expense can be achieved, and that among the three proposed method the one that decides on dummy events on the fly is (by far) the best.

This paper represents a paradigm shift in what advice agents should provide people. Contrary to what was previously thought, we empirically show that agents that dispense optimal advice will not necessary facilitate the best improvement in people's strategies. Instead, we claim that agents should at times suboptimally advise. We provide results demonstrating the effectiveness of a suboptimal advising approach in extensive experiments in two canonical mixed agent-human advice-giving domains. Our proposed guideline for suboptimal advising is to rely on the level of intuitiveness of the optimal advice as a measure for how much the suboptimal advice presented to the user should drift from the optimal value.

We present a credit-based matching mechanism for dynamic barter markets — and kidney exchange in particular — that is both strategy proof and efficient, that is, it guarantees truthful disclosure of donor-patient pairs from the transplant centers and results in the maximum global matching. Furthermore, the mechanism is individually rational in the sense that, in the long run, it guarantees each transplant center more matches than the center could have achieved alone. The mechanism does not require assumptions about the underlying distribution of compatibility graphs — a nuance that has previously produced conflicting results in other aspects of theoretical kidney exchange. Our results apply not only to matching via 2-cycles: the matchings can also include cycles of any length and altruist-initiated chains, which is important at least in kidney exchanges. The mechanism can also be adjusted to guarantee immediate individual rationality at the expense of economic efficiency, while preserving strategy proofness via the credits. This circumvents a well-known impossibility result in static kidney exchange concerning the existence of an individually rational, strategy-proof, and maximal mechanism. We show empirically that the mechanism results in significant gains on data from a national kidney exchange that includes 59% of all US transplant centers.

This paper considers the problem of cooperation between self-interested agents in acquiring better information regarding the nature of the different options and opportunities available to them. By sharing individual findings with others, the agents can potentially achieve a substantial improvement in overall and individual expected benefits. Unfortunately, it is well known that with self-interested agents equilibrium considerations often dictate solutions that are far from the fully cooperative ones, hence the agents do not manage to fully exploit the potential benefits encapsulated in such cooperation. In this paper we introduce, analyze and demonstrate the benefit of five methods aiming to improve cooperative information gathering. Common to all five that they constrain and limit the information sharing process. Nevertheless, the decrease in benefit due to the limited sharing is outweighed by the resulting substantial improvement in the equilibrium individual information gathering strategies. The equilibrium analysis given in the paper, which, in itself is an important contribution to the study of cooperation between self-interested agents, enables demonstrating that for a wide range of settings an improved individual expected benefit is achieved for all agents when applying each of the five methods.

Peer Designed Agents (PDAs), computer agents developed by non-experts, is an emerging technology, widely advocated in recent literature for the purpose of replacing people in simulations and investigating human behavior. Its main premise is that strategies programmed into these agents reliably reflect, to some extent, the behavior used by their programmers in real life. In this paper we show that PDA development has an important side effect that has not been addressed to date -- the process that merely attempts to capture one's strategy is also likely to affect the developer's strategy. The phenomenon is demonstrated experimentally, using several performance measures. This result has many implications concerning the appropriate design of PDA-based simulations, and the validity of using PDAs for studying individual decision making. Furthermore, we obtain that PDA development actually improved the developer's strategy according to all performance measures. Therefore, PDA development can be suggested as a means for improving people's problem solving skills.

The popularity of online shopping has contributed to the development of comparison shopping agents (CSAs) aiming to facilitate buyers' ability to compare prices of online stores for any desired product. Furthermore, the plethora of CSAs in today's markets enables buyers to query more than a single CSA when shopping, thus expanding even further the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase based on the prices outputted as a result of any single query, and consequently decreases each CSAs' expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers' prices, potentially resulting in a more attractive ``best price''. In this paper we suggest a complementary approach that improves the attractiveness of a CSA by presenting the prices to the user in a specific intelligent manner, which is based on known cognitive-biases.The advantage of this approach is its ability to affect the buyer's tendency to terminate her search for a better price, hence avoid querying further CSAs, without having the CSA spend any of its resources on finding better prices to present.The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments with people confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA.

Human operators play a key role in robotic exploration and search missions, as the interpretation of camera images typically requires the visual perception skills of humans. Thus one the key challenges in building effective robotic systems for such missions lies in developing good operator interfaces. In this paper, we present a novel asynchronous user-guided interface for human operators of robotic search of an unknown area. Enabled by efficient methods to store and retrieve recorded images (and meta information) in real-time, our interface allows the operator to click on any point of interest. The operator is then presented with highly-relevant images that cover the point, without occlusion. This, in contrast with system-guided approaches, where an automated system selects areas and images for inspection. Experiments with 32 human subjects in two different-size maps favor the user-guided approach we present over the system-guided approach. Additional experiments with human subjects provide an explanation as to the environment characteristics that favor our approach.

This paper deals with platforms that provide agents easier access to the type of opportunities in which they are interested (e.g., eCommerce platforms, used cars bulletins and dating web-sites). We show that under various common service schemes, a platform can benefit from not necessarily listing all the opportunities with which it is familiar, even if there is no marginal cost for listing any additional opportunity. The main implication of this result is that platforms should extract their expected-profit-maximizing service terms not based solely on the fees charged from users, but they should also use the subset that will be listed as the decision variable in the optimization problem. The analysis applies to four well-known service schemes that a platform may use to price its services. We show that neither of these schemes generally dominates the others or is dominated by any of the others. For the common case of homogeneous preferences, however, several dominance relationships can be proved, enabling the platform to identify the schemes that should be used as a default. Furthermore, the analysis provides a game-theoretic search-based explanation for a possible preference of buyers to pay for the service rather than receive it for free (e.g., when the service is sponsored by ads), a phenomena that has been justified in prior literature typically with the argument of willingness to pay a premium for an ad-free experience or more reliable platforms. The paper shows that this preference can hold both for the users and the platform in a given setting, even if both sides are fully strategic.

In this paper we empirically investigate the feasibility of using peer-designed agents (PDAs) instead of people for the purpose of mechanism evaluation. This approach has been increasingly advocated in agent research in recent years, mainly due to it

The blooming of comparison shopping agents (CSAs) in recent years enables buyers in today's markets to query more than a single CSA while shopping, thus substantially expanding the list of sellers whose prices they obtain. From the individual CSA point of view, however, the multi-CSAs querying is definitely non-favorable as most of today's CSAs benefit depends on payments they receive from sellers upon transferring buyers to their websites (and making a purchase). The most straightforward way for the CSA to improve its competence is through spending more resources on getting more sellers' prices, potentially resulting in a more attractive ``best price''. In this paper we suggest a complementary approach that improves the attractiveness of the best price returned to the buyer without having to extend the CSAs' price database. This approach, which we term ``selective price disclosure'' relies on removing some of the prices known to the CSA from the list of results returned to the buyer. The advantage of this approach is in the ability to affect the buyer's beliefs regarding the probability of obtaining more attractive prices if querying additional CSAs. The paper presents two methods for choosing the subset of prices to be presented to a fully-rational buyer, attempting to overcome the computational complexity associated with evaluating all possible subsets. The effectiveness and efficiency of the methods are demonstrated using real data, collected from five CSAs for four products. Furthermore, since people are known to have an inherently bounded rationality, the two methods are also evaluated with human buyers, demonstrating that selective price-disclosing can be highly effective with people, however the subset of prices that needs to be used should be extracted in a different (and more simplistic) manner.

This paper studies distributed cooperative multi-agent exploration methods in settings where the exploration is costly and the overall performance measure is determined by the minimum performance achieved by any of the individual agents. Such an exploration setting is applicable to various multi-agent systems, e.g., in Dynamic Spectrum Access exploration. The goal in such problems is to optimize the process as a whole, considering the tradeoffs between the quality of the solution obtained and the cost associated with the exploration and coordination between the agents. Through the analysis of the two extreme cases where coordination is completely free and when entirely disabled, we manage to extract the solution for the general case where coordination is taken to be costly, modeled as a fee that needs to be paid for each additional coordinated agent. The strategy structure for the general case is shown to be threshold-based, and the thresholds which are analytically derived in this paper can be calculated offline, resulting in a very low online computational load.

In this paper, we investigate the usefulness of peer-designed agents (PDAs) as a turn-key technology for enhancing parking simulations. The use of PDAs improves the system's ability to capture the dynamics of the interaction between individuals in the system, each theoretically exhibiting a different strategic behavior. Furthermore, since people in general are inherently rational and computation bounded, simulating this domain becomes even more challenging. The advantage of PDAs in this context lies in their ability to reliably simulate a large pool of human individuals with diverse strategies and goals. We demonstrate the efficacy of the proposed method by developing a large-scale simulation system for the parking space search domain, which plays an important role in urban transport systems. The system is based on 34 different parking search strategies. Most of these strategies are substantially different from synthetic strategies that are used in prior literature. A quantitative analysis of the PDAs indicates that they reliably capture their designers' real-life strategies. Finally, we demonstrate the usefulness of PDA-based parking space search simulation by utilizing it to evaluate four different information technologies that are of increasing use in recent years.

Motivated by applications in Dynamic Spectrum Access Networks, we focus on a system in which a few agents are engaged in a costly individual exploration process where each agent's benefit is determined according to the minimum obtained value. Such an

In this paper we study distributed agent matching in environments characterized by uncertain signals, costly exploration, and the presence of an information broker. Each agent receives information about the potential value of matching with others. This information signal may, however be noisy, and the agent incurs some cost in receiving it. If all candidate agents agree to the matching the team is formed and each agent receives the true unknown utility of the matching, and leaves the market. We consider the effect of the presence of information brokers, or experts, on the outcomes of such matching processes. Experts can, upon payment of a fee, perform the service of disambiguating noisy signals and revealing the true value of a match to any agent. We analyze equilibrium behavior given the fee set by a monopolist expert and use this analysis to derive the revenue maximizing strategy for the expert as the first mover in a Stackelberg game. Surprisingly, we find that better information can hurt: the presence of the expert, even if the use of its services is optional, can degrade both individual agents' utilities and overall social welfare. While in one-sided search the presence of the expert can only help, in two-sided (and general k-sided) search the externality imposed by the fact that others are consulting the expert can lead to a situation where the equilibrium outcome is that everyone consults the expert, even though all agents would be better off if the expert were not present. As an antidote, we show how market designers can enhance welfare by taxing use of expert services.

The paper focuses on mining clusters that are characterized by a lagged relationship between the data objects. We call such clusters lagged co-clusters. A lagged co-cluster of a matrix is a sub matrix determined by a subset of rows and their corresponding lag over a subset of columns. Extracting such subsets (not necessarily successive) may reveal an underlying governing regulatory mechanism. Such a regulatory mechanism is quite common in real life settings. It appears in a variety of fields: meteorology, seismic activity, stock market behavior, neuronal brain activity, river flow and navigation, are but a limited list of examples. Mining such lagged co-clusters not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal lagged co-cluster is an NP-complete problem. We present a polynomial-time Monte-Carlo algorithm for finding a set of lagged co-clusters whose error does not exceed a pre-specified value, which handles noise, anti-correlations, missing values, and overlapping patterns. Moreover, we prove that the list includes, with fixed probability, a lagged co-cluster which is optimal in its dimensions. The algorithm was extensively evaluated using various environments. First, artificial data, enabling the evaluation of specific, isolated properties of the algorithm. Secondly, real-world data, using river flow and topographic data, enabling the evaluation of the algorithm to efficiently mine relevant and coherent lagged co-clusters in environments that are temporal, i.e., time reading data, and non-temporal, respectively.

In this paper we show how taking advantage of autonomous agents' capability to maintain parallel interactions with others, and incorporating it into the cooperative economic search model results in a new search strategy which outperforms current strategies in use. As a framework for our analysis we use the electronic marketplace, where buyer agents have the incentive to search cooperatively. The new search technique is quite intuitive, however its analysis and the process of extracting the optimal search strategy are associated with several significant complexities. These difficulties are derived mainly from the unbounded search space and simultaneous dual affects of decisions taken along the search. We provide a comprehensive analysis of the model, highlighting, demonstrating and proving important characteristics of the optimal search strategy. Consequently, we manage to come up with an efficient modular algorithm for extracting the optimal cooperative search strategy for any given environment. A computational based comparative illustration of the system performance using the new search technique versus the traditional methods is given, emphasizing the main differences in the optimal strategy's structure and the advantage of using the proposed model.

This paper describes a novel programmatic approach to efficiently distribute resources in a dynamic cellular network, using local negotiations. Our proposed mechanism is reactive and facilitates parallel self-adaptation efforts, leading to dynamics that improve overall network performance. The local nature of the negotiations being performed as part of the adaptation process enables frequent changes in the network's parameters with a negligible coordination overhead. The results of our experiments suggest rapid adjustment to changes and overall improvement over time in the number of users served by the network. We evaluate our algorithm based on the service level index, measured by the number of covered handsets. Nevertheless, the proposed algorithm supports any set of parameters and any combination of performance measures supplied by service providers.