Incentive Design

Mechanism design and game theory for e-commerce platforms, team formation, and kidney exchange — aligning individual incentives with collective outcomes.

Headline result: Mechanisms for platforms, teams, and kidney exchange.

Papers: 21
Years: 2013–2022
Co-authors: 20

Overview

Rational agents pursue their own interests — but well-designed rules can make selfish behavior lead to good collective outcomes. We apply mechanism design and game theory to build systems where honesty and cooperation are the optimal strategy, from e-commerce to organ donation.

Research Areas

Strategic Information Disclosure in E-Commerce — Disclosure mechanisms that improve buyer decisions and platform welfare simultaneously on platforms like Amazon, without sacrificing honesty or profit.
Strategy-Proof Team Formation — Mechanisms where truth-telling about skills is the dominant strategy, guaranteeing effective team composition even when participants have incentives to misreport.
Kidney Exchange Optimization — Credit-based incentive systems for paired donation programs that prevent strategic withholding of compatible pairs, increasing overall transplant rates while preserving fairness.

Technical Approach

Our work combines classical mechanism design theory (VCG, strategyproofness, incentive compatibility) with computational methods — simulations, algorithmic game theory, and empirical behavioral analysis. Each mechanism comes with formal proofs and is validated against real-world platform data.

Impact

The information disclosure mechanisms have been implemented in real e-commerce settings. Kidney exchange work contributes to national transplant policy discussions. Team formation mechanisms are deployed in organizational settings involving thousands of participants.

People on this line

The 8 most frequent of 20 co-authors on this line since 2013.

Ranked by joint papers in this line. The bar under each name spans their years on it, 2013 to 2022. 12 more co-authors on the Lab page

Papers

21 publications, newest first.

All publications

2022

Selected: A Rotating Proposer Mechanism for Team Formation

Jian Lou, Chen Hajaj, and Yevgeniy Vorobeychik

PreprintarXiv preprint arXiv:2204.04251

DOI

We present a rotating proposer mechanism for team formation, which implements a Pareto efficient subgame perfect Nash equilibrium of an extensive-form team formation game.
```
@misc{low2022a,
  author = {Lou, Jian and Hajaj, Chen and Vorobeychik, Yevgeniy},
  title = {A Rotating Proposer Mechanism for Team Formation},
  howpublished = {arXiv preprint arXiv:2204.04251},
  year = {2022},
  doi = {10.48550/arXiv.2204.04251},
  url = {https://doi.org/10.48550/arXiv.2204.04251}
}
```

2021

Robust Coordination in Adversarial Social Networks: From Human Behavior to Agent-Based Modeling

Chen Hajaj, Zlatko Joveski, Sixie Yu, and Yevgeniy Vorobeychik

JournalNetwork Science SJR Q1

DOI

AbstractDecentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.
```
@article{hajaj2021robust,
  title = {Robust Coordination in Adversarial Social Networks: From Human Behavior to Agent-Based Modeling},
  author = {Hajaj, Chen and Joveski, Zlatko and Yu, Sixie and Vorobeychik, Yevgeniy},
  journal = {Network Science},
  volume = {9},
  number = {3},
  pages = {255--290},
  year = {2021},
  publisher = {Cambridge University Press},
  doi = {10.1017/nws.2021.5}
}
```

2019

Adversarial Coordination on Social Networks

Chen Hajaj, Sixie Yu, Zlatko Joveski, and Yevgeniy Vorobeychik

ConferenceInternational Conference on Autonomous Agents and Multiagent Systems (AAMAS) CORE A*

DOI

Extensive literature exists studying decentralized coordination and consensus, with considerable attention devoted to ensuring robustness to faults and attacks. However, most of the latter literature assumes that non-malicious agents follow simple stylized rules. In reality, decentralized protocols often involve humans, and understanding how people coordinate in adversarial settings is an open problem. We initiate a study of this problem, starting with a human subjects investigation of human coordination on networks in the presence of adversarial agents, and subsequently using the resulting data to bootstrap the development of a credible agent-based model of adversarial decentralized coordination. In human subjects experiments, we observe that while adversarial nodes can successfully prevent consensus, the ability to communicate can significantly improve robustness, with the impact particularly significant in scale-free networks. On the other hand, and contrary to typical stylized models of behavior, we show that the existence of trusted nodes has limited utility. Next, we use the data collected in human subject experiments to develop a data-driven agent-based model of adversarial coordination. We show that this model successfully reproduces observed behavior in experiments, is robust to small errors in individual agent models, and illustrate its utility by using it to explore the impact of optimizing network location of trusted and adversarial nodes.
```
@inproceedings{hajaj2019adversarial,
  title = {Adversarial Coordination on Social Networks},
  author = {Hajaj, Chen and Yu, Sixie and Joveski, Zlatko and Vorobeychik, Yevgeniy},
  booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  pages = {1515--1523},
  year = {2019},
  doi = {10.65109/ckdj6887}
}
```
Non-Cooperative Team Formation and a Team Formation Mechanism

Yevgeniy Vorobeychik, Myrna Wooders, Greg Leo, Matthew Chambers, Chen Hajaj, Jian Lou, and Martin Linden

ConferenceThe 11th Annual Conference of the Israeli Chapter of the Game Theory Society

DOI

We model decentralized team formation as a game in which players make offers to potential teams whose members then either accept or reject the offers. The games induce no-delay subgame perfect equilibria with unique outcomes that are individually rational and match soulmates. We provide sufficient conditions for equilibria to implement core coalition structures, and show that when each player can make a sufficiently large number of proposals, outcomes are Pareto optimal. We then design a mechanism to implement equilibrium of this game and provide sufficient conditions to ensure that truthful reporting of preferences is a strong ex post Nash equilibrium. Moreover, we show empirically that players rarely have an incentive to misreport preferences more generally. Furthermore, for the problem with cardinal preferences, we show empirically that the resulting mechanism results in significantly higher social welfare than serial dictatorship, and the outcomes are highly equitable.
```
@inproceedings{chambers2017non,
  title = {Non-Cooperative Team Formation and a Team Formation Mechanism},
  booktitle = {The 11th Annual Conference of the Israeli Chapter of the Game Theory Society},
  note = {Working paper: SSRN 3054771},
  author = {Vorobeychik, Yevgeniy and Wooders, Myrna and Leo, Greg and Chambers, Matthew and Hajaj, Chen and Lou, Jian and Van der Linden, Martin},
  year = {2019},
  doi = {10.2139/ssrn.3054771}
}
```

Adversarial Randomized Task Assignment

Chen Hajaj and Yevgeniy Vorobeychik

Workshop6th Day on Computational Game Theory

@inproceedings{hajaj2019randomized,
  title = {Adversarial Randomized Task Assignment},
  author = {Hajaj, Chen and Vorobeychik, Yevgeniy},
  booktitle = {6th Day on Computational Game Theory},
  year = {2019}
}

2018

Adversarial task assignment

Chen Hajaj and Yevgeniy Vorobeychik

ConferenceInternational Joint Conference on Artificial Intelligence (IJCAI) CORE A*

DOI

The problem of assigning tasks to workers is of long-standing fundamental importance. Examples of this include the classical problem of assigning computing tasks to nodes in a distributed computing environment, assigning jobs to robots, and crowdsourcing. Extensive research into this problem generally addresses important issues such as uncertainty and incentives. However, the problem of adversarial tampering with the task assignment process has not received as much attention. We are concerned with a particular adversarial setting where an attacker may target a set of workers in order to prevent the tasks assigned to these workers from being completed. When all tasks are homogeneous, we provide an efficient algorithm for computing the optimal assignment. When tasks are heterogeneous, we show that the adversarial assignment problem is NP-Hard, and present an algorithm for solving it approximately. Our theoretical results are accompanied by extensive experiments showing the effectiveness of our algorithms.
```
@inproceedings{hajaj2018adversarial,
  title = {Adversarial task assignment},
  author = {Hajaj, Chen and Vorobeychik, Yevgeniy},
  booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
  pages = {3783--3789},
  year = {2018},
  doi = {10.24963/ijcai.2018/526}
}
```
Adversarial Coordination on Social Networks

Chen Hajaj, Sixie Yu, Zlatko Joveski, and Yevgeniy Vorobeychik

WorkshopAAMAS-IJCAI Workshop on Agents and Incentives in Artificial Intelligence (AI³)

Extensive literature exists studying decentralized coordination and consensus, with considerable attention devoted to ensuring robustness to faults and attacks. However, most of the latter literature assumes that non-malicious agents follow simple stylized rules. In reality, decentralized protocols often involve humans, and understanding how people coordinate in adversarial settings is an open problem. We initiate a study of this problem, starting with a human subjects investigation of human coordination on networks in the presence of adversarial agents, and subsequently using the resulting data to bootstrap the development of a credible agent-based model of adversarial decentralized coordination. In human subjects experiments, we observe that while adversarial nodes can successfully prevent consensus, the ability to communicate can significantly improve robustness, with the impact particularly significant in scale-free networks. On the other hand, and contrary to typical stylized models of behavior, we show that the existence of trusted nodes has limited utility. Next, we use the data collected in human subject experiments to develop a data-driven agent-based model of adversarial coordination. We show that this model successfully reproduces observed behavior in experiments, is robust to small errors in individual agent models, and illustrate its utility by using it to explore the impact of optimizing network location of trusted and adversarial nodes.
```
@inproceedings{hajaj2018coordination,
  title = {Adversarial Coordination on Social Networks},
  author = {Hajaj, Chen and Yu, Sixie and Joveski, Zlatko and Vorobeychik, Yevgeniy},
  booktitle = {AAMAS-IJCAI Workshop on Agents and Incentives in Artificial Intelligence (AI³)},
  year = {2018}
}
```

Automated Mechanism Design for the Roommates Problem

Jian Lou, Chen Hajaj, Myrna Wooders, and Yevgeniy Vorobeychik

WorkshopAAMAS-IJCAI Workshop on Agents and Incentives in Artificial Intelligence (AI³)

@inproceedings{lou2018roommates,
  title = {Automated Mechanism Design for the Roommates Problem},
  author = {Lou, Jian and Hajaj, Chen and Wooders, Myrna and Vorobeychik, Yevgeniy},
  booktitle = {AAMAS-IJCAI Workshop on Agents and Incentives in Artificial Intelligence (AI³)},
  year = {2018}
}

2017

Enhancing Comparison Shopping Agents Through Ordering and Gradual Information Disclosure

Chen Hajaj, Noam Hazon, and David Sarne

JournalAutonomous Agents and Multi-Agent Systems SJR Q2

DOI

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, 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 the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.
```
@article{hajaj2017enhancing,
  title = {Enhancing Comparison Shopping Agents Through Ordering and Gradual Information Disclosure},
  author = {Hajaj, Chen and Hazon, Noam and Sarne, David},
  journal = {Autonomous Agents and Multi-Agent Systems},
  volume = {31},
  number = {3},
  pages = {696--714},
  year = {2017},
  publisher = {Springer US},
  doi = {10.1007/s10458-016-9342-8}
}
```
Selective Opportunity Disclosure at the Service of Strategic Information Platforms

Chen Hajaj and David Sarne

JournalAutonomous Agents and Multi-Agent Systems SJR Q2

DOI

This paper studies the strategic behavior of 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 four 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 take the subset of opportunities to be included in their listings as a decision variable, alongside the fees set for the service in their expected-profit maximizing optimization problem. We show that none of the four schemes generally dominates any of the others or is dominated by any. For the case of homogeneous preferences, however, several dominance relationships can be proved. 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). The paper shows that this preference can hold both for the users and the platform simultaneously in a given setting, even if both sides are fully strategic. Finally, the paper analyzes the potential improvement in the platform’s expected profit that can be achieved by considering hybrid service schemes that combine the basic ones. In particular, we focus in the Two-Part Tariff scheme that combines the two commonly used subscription and pay-per-click schemes.
```
@article{hajaj2017selective,
  title = {Selective Opportunity Disclosure at the Service of Strategic Information Platforms},
  author = {Hajaj, Chen and Sarne, David},
  journal = {Autonomous Agents and Multi-Agent Systems},
  volume = {31},
  number = {5},
  pages = {1133--1164},
  year = {2017},
  publisher = {Springer US},
  doi = {10.1007/s10458-016-9357-1}
}
```
Enhancing Crowdworkers’ Vigilance

Avshalom Elmalech, David Sarne, Esther David, and Chen Hajaj

ConferenceInternational Joint Conference on Artificial Intelligence (IJCAI) CORE A*

DOI

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.
```
@inproceedings{elmalech2017enhancing,
  title = {Enhancing Crowdworkers' Vigilance},
  author = {Elmalech, Avshalom and Sarne, David and David, Esther and Hajaj, Chen},
  booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
  pages = {4826--4830},
  year = {2017},
  doi = {10.24963/ijcai.2017/675}
}
```
Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets

Sofia Ceppi, Esther David, Chen Hajaj, Valentin Robu, and Ioannis A Vetsikas

BookEdited volume, AMEC/TADA 2015 and 2016 revised selected papers, Springer

DOI

This book constitutes revised selected papers from the 17th and 18th International Workshop on Agent-Mediated Electronic Commerce, AMEC/TADA 2015 and 2016, which took place in Istanbul, Turkey, in May 2015, and in New York City, USA, in July 2016. The 10 papers presented in this volume were carefully reviewed and selected for inclusion in the book. Both workshops aim to present a cross-section of the state of the art in automated electronic markets and encourage theoretical and empirical work that deals with both the individual agent level as well as the system level.
```
@book{ceppi2017agent,
  title = {Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets},
  doi = {10.1007/978-3-319-54229-4},
  note = {Edited volume, AMEC/TADA 2015 and 2016 revised selected papers},
  series = {Lecture Notes in Business Information Processing},
  author = {Ceppi, Sofia and David, Esther and Hajaj, Chen and Robu, Valentin and Vetsikas, Ioannis A},
  year = {2017},
  publisher = {Springer}
}
```

Rotating Proposer Mechanisms for Team Formation

Jian Lou, Martin Linden, Pranav Batra, Chen Hajaj, Greg Leo, Yevgeniy Vorobeychik, and Myrna Wooders

WorkshopWorkshop on Cooperative Games in Multiagent Systems (CoopMAS)

@inproceedings{lou2017rotating,
  title = {Rotating Proposer Mechanisms for Team Formation},
  author = {Lou, Jian and Van der Linden, Martin and Batra, Pranav and Hajaj, Chen and Leo, Greg and Vorobeychik, Yevgeniy and Wooders, Myrna},
  booktitle = {Workshop on Cooperative Games in Multiagent Systems (CoopMAS)},
  year = {2017},
  note = {Most Visionary Paper Award}
}

2016

Extending Workers’ Attention Span Through Dummy Events

Avshalom Elmalech, David Sarne, Esther David, and Chen Hajaj

ConferenceProceedings of the AAAI Conference on Human Computation and Crowdsourcing

DOI

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.
```
@inproceedings{elmalech2016extending,
  doi = {10.1609/hcomp.v4i1.13276},
  title = {Extending Workers' Attention Span Through Dummy Events},
  author = {Elmalech, Avshalom and Sarne, David and David, Esther and Hajaj, Chen},
  booktitle = {Proceedings of the AAAI Conference on Human Computation and Crowdsourcing},
  volume = {4},
  pages = {42--51},
  year = {2016}
}
```
Intelligent Mechanisms for Platforms in Modern Markets

Chen Hajaj

ThesisPhD thesis, Bar-Ilan University

DOI

This thesis explores the use of autonomous agents serving as mediators between different entities in modern markets to maximize an expected revenue (e.g., the agents’ own profit or users’ social welfare). This mediation can take many forms and serve many purposes, such as buyers and sellers at online shopping environments, men and women at dating sites, and different traders at barter markets. These agents differ from one another in terms such as the cost of their mediation services (e.g, they can offer their services for a fee or complimentary). In the first case, when the services are costly, agents must ponder the terms they set for their services. Similarly, when offering their services for free they have to be as appealing as possible in order to overcome external competition. This thesis provides theoretically-justified and empirically-validated approaches for designing and operating intelligent mechanisms for mediation agents in both electronic and barter markets. While two of the mechanisms presented within this thesis guide the platform for how to use selective information disclosure for its benefit, a different one is designed specifically in order to avoid selective information disclosure to the platform. The research is based on both theoretical analysis and online validation of suggested heuristics. Theoretical analysis is carried out using concepts from search theory, and game theory and the online validation is based on Amazon Mechanical Turk, a well-known crowdsourcing platform imitating people’s behavior in real-life.The models used in this thesis consider settings of autonomous, fully-rational, and self-interested agents interacting with both fully-rational agents and bounded-rational people, and how the difference in their decision-making processes may imply differently, yet correlated, strategies for the system designer. The different model variants are applicable and can be mapped onto various different real-life applications (e.g., online shopping, car purchasing, and barter markets). The models differ primarily in the assumptions they make. In electronic markets, assumptions relating to the agents’ source of income and the users’ computational capabilities (fully-rational agents vs. bounded-rational people). For barter markets, this thesis focuses on fully-rational agents that differ in the number of goods they hold and can be traded on the market. Based on the common model for these markets, this thesis provides a proof of impossibility result on the ability to enforce the agents’ strategy-proof behavior regardless of the means used. Still, the thesis shows that by adding a set of real-life assumptions on the model, one can circumvent this impossibility result and validate that the truthful strategy is indeed the dominant one. The analysis is accompanied by extensive simulations and online experiments, making use of various testbeds. The simulations and online experiments are used to validate the applicability of the suggested mechanisms for modern markets and to the different entities populating them.
```
@phdthesis{hajaj2016intelligent,
  title = {Intelligent Mechanisms for Platforms in Modern Markets},
  author = {Hajaj, Chen},
  year = {2016},
  school = {Bar-Ilan University},
  doi = {10.31237/osf.io/n5vbs}
}
```

2015

Improving Comparison Shopping Agents’ Competence Through Selective Price Disclosure

Chen Hajaj, Noam Hazon, and David Sarne

JournalElectronic Commerce Research and Applications SJR Q1

DOI

A new approach for increasing the attractiveness of comparison shopping agents.The approach, selective price-disclosure, affects the buyer’s decision-making.Two methods for selective price-disclosure for fully-rational agents are given.The effectiveness and efficiency of the methods are demonstrated using real data.A third method is experimentally shown to be highly effective with people. The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, and an inter-CSAs competition naturally arises. We suggest a new approach, termed "selective price disclosure", which improves the attractiveness of a CSA by removing some of the prices in the outputted list. The underlying idea behind this approach is to affect the buyer’s beliefs regarding the chance of obtaining more attractive prices. The paper presents two methods, which are suitable for fully-rational buyers, for deciding which prices among those known to the CSA should be disclosed. The effectiveness and efficiency of the methods are evaluated using real data collected from five CSAs. The methods are also evaluated with human subjects, showing that selective price disclosure can be highly effective in this case as well; however, the disclosed subset of prices should be extracted in a different (simplistic) manner.
```
@article{hajaj2015improving,
  title = {Improving Comparison Shopping Agents' Competence Through Selective Price Disclosure},
  author = {Hajaj, Chen and Hazon, Noam and Sarne, David},
  journal = {Electronic Commerce Research and Applications},
  volume = {14},
  number = {6},
  pages = {563--581},
  year = {2015},
  publisher = {Elsevier},
  doi = {10.1016/j.elerap.2015.08.006}
}
```
Strategy-Proof and Efficient Kidney Exchange Using a Credit Mechanism

Chen Hajaj, John P Dickerson, Avinatan Hassidim, Tuomas Sandholm, and David Sarne

ConferenceAAAI Conference on Artificial Intelligence (AAAI) CORE A*

DOI

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.
```
@inproceedings{hajaj2015strategy,
  doi = {10.1609/aaai.v29i1.9322},
  title = {Strategy-Proof and Efficient Kidney Exchange Using a Credit Mechanism},
  author = {Hajaj, Chen and Dickerson, John P and Hassidim, Avinatan and Sandholm, Tuomas and Sarne, David},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2015}
}
```

2014

Advanced Service Schemes for a Self-Interested Information Platform

Chen Hajaj, David Sarne, and Lea Perets

WorkshopInternational Conference on Autonomous Agents and Multiagent Systems (AAMAS)

DOI

This paper deals with platforms that bring together agents and opportunities of the type in which they are interested (e.g., eCommerce platforms, used car bulletins and dating web sites). It shows that the platform can benefit from not necessarily listing all of the opportunities that it can potentially list, even if there is no marginal cost for listing any additional opportunity. Two important implications of this result are discussed and demonstrated. The first is that the platform should take into account the option to disclose different subsets of opportunities, whenever setting its expected-profit-maximizing service fee. The second is that from the platform’s users point of view, it might turn out to be more beneficial to pay for the platform’s service rather than get it for free (e.g., when the service is sponsored by ads), as the costly case is characterized by more favorable listings.
```
@inproceedings{hajaj2014advanced,
  doi = {10.65109/dumc9735},
  title = {Advanced Service Schemes for a Self-Interested Information Platform},
  author = {Hajaj, Chen and Sarne, David and Perets, Lea},
  booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  pages = {1413--1414},
  year = {2014}
}
```
Ordering Effects and Belief Adjustment in the Use of Comparison Shopping Agents

Chen Hajaj, Noam Hazon, and David Sarne

ConferenceAAAI Conference on Artificial Intelligence (AAAI) CORE A*

DOI

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 peoplein a way that is highly advantageous to the CSA.
```
@inproceedings{hajaj2014ordering,
  doi = {10.1609/aaai.v28i1.8837},
  title = {Ordering Effects and Belief Adjustment in the Use of Comparison Shopping Agents},
  author = {Hajaj, Chen and Hazon, Noam and Sarne, David},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  pages = {930--936},
  year = {2014}
}
```
Strategic Information Platforms: Selective Disclosure and the Price of Free

Chen Hajaj and David Sarne

ConferenceACM Conference on Economics and Computation (EC) CORE B

DOI

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.
```
@inproceedings{hajaj2014strategic,
  title = {Strategic Information Platforms: Selective Disclosure and the Price of Free},
  author = {Hajaj, Chen and Sarne, David},
  booktitle = {ACM Conference on Economics and Computation (EC)},
  pages = {839--856},
  year = {2014},
  organization = {ACM},
  doi = {10.1145/2600057.2602864}
}
```

2013

Search More, Disclose Less

Chen Hajaj, Noam Hazon, David Sarne, and Avshalom Elmalech

ConferenceAAAI Conference on Artificial Intelligence (AAAI) CORE A*

DOI

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.
```
@inproceedings{hajaj2013search,
  doi = {10.1609/aaai.v27i1.8607},
  title = {Search More, Disclose Less},
  author = {Hajaj, Chen and Hazon, Noam and Sarne, David and Elmalech, Avshalom},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2013}
}
```

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