Agent development as a strategy shaper

Elmalech, Avshalom; Sarne, David; Agmon, Noa

doi:10.1007/s10458-015-9299-z

Cited by 6 publications

(2 citation statements)

References 31 publications

(53 reference statements)

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“…The properties, benefits and influence of belief revision has been widely discussed in Human-Computer Interaction [58,103] and artificial intelligence [81,167,51] literature. In particular, in the multi-agent systems domain, Elmalech et al [53] and Azaria et al [17,15,18] investigate the ability to influence the user's beliefs. Nevertheless, these works require learning the user or the development of peer-designed agents (PDAs) for this specific purpose.…”

Section: Electronic Commercementioning

confidence: 99%

Intelligent Mechanisms for Platforms in Modern Markets

Hajaj¹

2019

Preprint

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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.

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Section: Electronic Commercementioning

confidence: 99%

Intelligent Mechanisms for Platforms in Modern Markets

Hajaj¹

2019

Preprint

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“…For example, Rahman (2012) suggests adding a higher level or monitoring task (Rahman 2012). The difference from our work is that it considers fully rational players, while we consider human workers that are known to act irrationally (Hajaj, Hazon, and Sarne 2015;Elmalech, Sarne, and Agmon 2016;Elmalech, Sarne, and Grosz 2015;.…”

Section: Related Workmentioning

confidence: 99%

Extending Workers' Attention Span Through Dummy Events

Elmalech

Sarne

David

et al. 2016

HCOMP

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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.

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