Han Sung Kim (April 1st 2010). Mechanism Design of Haptic Devices, Advances in Haptics, Mehrdad Hosseini Zadeh, IntechOpen, DOI: 10.5772/8697.

New Zealand

From skipped exercise classes to last-minute cancellation of dentist appointments, underutilization of reserved resources abounds. Likely reasons include uncertainty about the future, further exacerbated by present bias. In this paper, we unite resource allocation and commitment devices through the design of contingent payment mechanisms, and propose the two-bid penalty-bidding mechanism. This extends an earlier mechanism proposed by Ma et al. (2019), assigning the resources based on willingness to accept a no-show penalty, while also allowing each participant to increase her own penalty in order to counter present bias. We establish a simple dominant strategy equilibrium, regardless of an agent&#39;s level of present bias or degree of &quot;sophistication&quot;. Via simulations, we show that the proposed mechanism substantially improves utilization and achieves higher welfare and better equity in comparison with mechanisms used in practice and mechanisms that optimize welfare in the absence of present bias.

AAMAS 2020

Penalty Bidding Mechanisms for Allocating Resources and Overcoming Present Bias

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

From skipped exercise classes to last-minute cancellation of dentist appointments, underutilization of reserved resources abounds. Likely reasons include uncertainty about the future, further exacerbated by present bias. In this paper, we unite resource allocation and commitment devices through the design of contingent payment mechanisms, and propose the two-bid penalty-bidding mechanism. This extends an earlier mechanism proposed by Ma et al. (2019), assigning the resources based on willingness to accept a no-show penalty, while also allowing each participant to increase her own penalty in order to counter present bias. We establish a simple dominant strategy equilibrium, regardless of an agent's level of present bias or degree of "sophistication". Via simulations, we show that the proposed mechanism substantially improves utilization and achieves higher welfare and better equity in comparison with mechanisms used in practice and mechanisms that optimize welfare in the absence of present bias.

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Many websites that recommend various services use crowdsourcing to collect reviews and rankings. These rankings, usually concerning a subset of all the offered alternatives, are then aggregated. Motivated by such scenarios, we axiomatise a family of positional scoring rules for profiles of possibly incomplete individual preferences. Many opportunities arise for the agents to manipulate the outcome in this setting. They may lie in order to obtain a better result by: (i) switching the order of a ranked pair of alternatives, (ii) omitting some of their truthful preferences, or (iii) reporting more preferences than the ones they truthfully hold. After formalising these new concepts, we characterise all positional scoring rules that are immune to manipulation.

Strategic Manipulation with Incomplete Preferences: Possibilities and Impossibilities for Positional Scoring Rules

Vaccination is a standard public health intervention for controlling the spread of epidemics. However, the supply of vaccines is typically limited, and therefore, their deployment needs to be optimized. Further, vaccines are produced over time, so the strategies have to be temporal. We study the problem \prob{} of designing vaccination strategies, within available budget constraints, to minimize the spread of an outbreak. This is a challenging stochastic optimization problem. We design a bicriteria approximation algorithm, which combines a linear programming based rounding, along with the sample average approximation technique. Our approach also provides the empirical approximation factor for the problem instance, relative to the optimum. We find that the approximation factor is significantly better than the worst case bound, and, in practice, is a small constant factor. Further, our method shows significantly better performance than all prior heuristics for this problem. With additional pruning techniques, we are able to scale our algorithm to networks with millions of edges.

Designing Effective and Practical Interventions to Contain Epidemics

We propose a game-theoretic approach to generalizing the classical Schelling model. At the core of our model are two features that did not receive much attention before. First, we allow multiple individuals to occupy the same location. Second, each individual's choice of location is influenced by their social network neighbors that also choose the same location. In addition, an individual's choice is influenced by others in the adjacent locations in a network-structured way, which captures the main spirit of the classical Schelling model and its numerous extensions. Our solution concept is a stable configuration represented as a pure-strategy Nash equilibrium (PSNE). We show that even for various special cases of the problem, computing or counting PSNE is provably hard. We give algorithms for computing PSNE, including efficient algorithms for several special cases. We highlight some of the attractive features of our model, such as predicting very few PSNE, through experiments.

Schelling Models with Localized Social Influence: A Game-Theoretic Framework

Report-sensitive spot-checking in peer grading systems

We develop a methodology for comparing two or more agent-based models that are developed for the same domain, but may differ in the particular data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to qualitatively different behaviors in the models. As an example, we develop an active learning algorithm to learn phase transition boundaries in contagion processes in order to compare two agent-based models of rooftop solar panel adoption.

An Active Learning Method for the Comparison of Agent-based Models

Deployment of a Plug-in multi-agent system for

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. The aim of my PhD thesis is to develop new techniques to model and quantitatively compare observed robot behaviors with nominal ones. My goal is to propose approaches for detecting anomalous behaviors of robot systems involved in complex long-term autonomy scenarios, both online, while robots are operating, and offline, after robots have completed a run of their tasks.

Modeling and Comparing Robot Behaviors for Anomaly Detection

Conventional Traffic Management System

a-Rank: Practically Scaling a-Rank through Stochatic Optimisation

A dark pool is a private forum for trading financial instruments such as equities and derivatives. The service is offered to traders, often representing large financial institutions, who aim to make large trades without telegraphing their intentions in a public venue such as the New York Stock Exchange or NASDAQ. Internally, the dark pool operates just like a public exchange, but the order book, a list of offers to buy and sell, is not visible to any of the participants. Only when matching orders are found the trade is executed and reported externally. Because the large orders are not visible to others, the risk of significant price moves is reduced. 

The operators of dark pools are trustworthy, but traders are notoriously suspicious. Traders are concerned about any ``leakage'' of information that might alert others to their intentions and cause price moves against them. The operator sees all orders posted by all participants, including sensitive information such as the volume of the order and the bid or ask price. Because the operator of the dark pool is usually a large financial institution with its own investments in the assets being traded, it may have conflicts of interest with the clients it serves in the dark pool. While such conflicts are mitigated by law and regulation, they continue to exist.

We propose new mechanism that reduce the need of traders to trust the operator of the dark pool. By adopting cryptographic techniques, we achieve a fully private and secure marketplace where sellers and buyers interact with the operator without exposing the volumes or prices of their orders to the operator or to any other counter party. These values remain secret until a matching order is found, and only afterward it will be revealed publicly. This provides a strictly stronger privacy guarantee compared to ordinary dark pools, and may lead to an increase in the trust of the participants to the system and reduce tension between participants and the operator.

Penalty Bidding Mechanisms for Allocating Resources and Overcoming Present Bias

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Strategic Manipulation with Incomplete Preferences: Possibilities and Impossibilities for Positional Scoring Rules

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Penalty Bidding Mechanisms for Allocating Resources and Overcoming Present Bias

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Next from AAMAS 2020

Strategic Manipulation with Incomplete Preferences: Possibilities and Impossibilities for Positional Scoring Rules

Similar lecture

Unbalanced CO-Optimal Transport

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