scholarly journals Dynamic Incentive Mechanisms

AI Magazine ◽  
2010 ◽  
Vol 31 (4) ◽  
pp. 79 ◽  
Author(s):  
David C. Parkes ◽  
Ruggiero Cavallo ◽  
Florin Constantin ◽  
Satinder Singh
Keyword(s):  
Decision Making ◽  
Mechanism Design ◽  
Intelligent Agents ◽  
Design Theory ◽  
Sequential Decision ◽  
Ongoing Research ◽  
Incentive Mechanisms ◽  
Dynamic Mechanism Design ◽  
Recent Developments ◽  
Automated Decision Making

Much of AI is concerned with the design of intelligent agents. A complementary challenge is to understand how to design “rules of encounter” by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural development, as AI is increasingly used for automated decision making in real-world settings. As we extend the ideas of mechanism design from economic theory, the mechanisms (or rules) become algorithmic and many new challenges surface. Starting with a short background on mechanism design theory, the aim of this paper is to provide a nontechnical exposition of recent results on dynamic incentive mechanisms, which provide rules for the coordination of agents in sequential decision problems. The framework of dynamic mechanism design embraces coordinated decision-making both in the context of uncertainty about the world external to an agent and also in regard to the dynamics of agent preferences. In addition to tracing some recent developments, we point to ongoing research challenges.

scholarly journals Dynamic Mechanism Design: An Introduction

2019 ◽  
Vol 57 (2) ◽  
pp. 235-274 ◽  
Author(s):  
Dirk Bergemann ◽  
Juuso Välimäki
Keyword(s):  
Mechanism Design ◽  
Dynamic Environment ◽  
Limited Liability ◽  
Dynamic Mechanism ◽  
Dynamic Mechanism Design ◽  
Dynamic Mechanisms ◽  
Sequential Screening ◽  
Open Questions ◽  
Recent Developments ◽  
Primary Focus

We provide an introduction to the recent developments of dynamic mechanism design, with a primary focus on the quasilinear case. First, we describe socially optimal (or efficient) dynamic mechanisms. These mechanisms extend the well-known Vickrey– Clark–Groves and D’Aspremont–Gérard–Varet mechanisms to a dynamic environment. Second, we discuss revenue optimal mechanisms. We cover models of sequential screening and revenue-maximizing auctions with dynamically changing bidder types. We also discuss models of information management where the mechanism designer can control (at least partially) the stochastic process governing the agents’ types. Third, we consider models with changing populations of agents over time. After discussing related models with risk-averse agents and limited liability, we conclude with a number of open questions and challenges that remain for the theory of dynamic mechanism design. ( JEL D44, D81, D82)

Preference Construction, Sequential Decision Making, and Trade Space Exploration

2013 ◽  
Author(s):  
Simon W. Miller ◽  
Timothy W. Simpson ◽  
Michael A. Yukish ◽  
Lorri A. Bennett ◽  
Sara E. Lego ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Design Decision ◽  
Sequential Process ◽  
Sequential Decision ◽  
Ongoing Research ◽  
Preference Construction ◽  
Sequential Decision Process ◽  
Preliminary Model ◽  
Trade Space Exploration

This paper develops and explores the interface between two related concepts in design decision making. First, design decision making is a process of simultaneously constructing one’s preferences while satisfying them. Second, design using computational models (e.g., simulation-based design and model-based design) is a sequential process that starts with low fidelity models for initial trades and progresses through models of increasing detail. Thus, decision making during design should be treated as a sequential decision process rather than as a single decision problem. This premise is supported by research from the domains of behavioral economics, psychology, judgment and decision making, neuroeconomics, marketing, and engineering design as reviewed herein. The premise is also substantiated by our own experience in conducting trade studies for numerous customers across engineering domains. The paper surveys the pertinent literature, presents supporting case studies and identifies use cases from our experiences, synthesizes a preliminary model of the sequential process, presents ongoing research in this area, and provides suggestions for future efforts.

scholarly journals Delegating learning

2021 ◽  
Vol 16 (2) ◽  
pp. 571-603
Author(s):  
Juan F. Escobar ◽  
Qiaoxi Zhang
Keyword(s):  
Decision Making ◽  
Mechanism Design ◽  
Design Problem ◽  
Dynamic Mechanism ◽  
Organizational Decision Making ◽  
Trade Off ◽  
Dynamic Mechanism Design ◽  
Optimal Delegation ◽  
Over Time

Learning is crucial to organizational decision making but often needs to be delegated. We examine a dynamic delegation problem where a principal decides on a project with uncertain profitability. A biased agent, who is initially as uninformed as the principal, privately learns the profitability over time and communicates to the principal. We formulate learning delegation as a dynamic mechanism design problem and characterize the optimal delegation scheme. We show that private learning gives rise to the trade‐off between how much information to acquire and how promptly it is reflected in the decision. We discuss implications on learning delegation for distinct organizations.

scholarly journals Dynamic Incentives in Wait List Mechanisms

2018 ◽  
Vol 108 ◽  
pp. 341-347
Author(s):  
Nikhil Agarwal ◽  
Itai Ashlagi ◽  
Paulo Somaini ◽  
Daniel Waldinger
Keyword(s):  
Mechanism Design ◽  
Design Theory ◽  
Decision Rules ◽  
Deceased Donor ◽  
Wait List ◽  
Dynamic Mechanism Design ◽  
Dynamic Incentives ◽  
Deceased Donor Kidney ◽  
Public Resources ◽  
Show Evidence

Many scarce public resources are allocated through wait lists that use priorities for individual agents. A new priority system for allocating deceased donor kidneys was adopted in 2014. This redesign was guided by simulations that held decision-rules fixed. We synthesize recent theoretical results to show that the welfare effects of a mechanism depend on the interaction between dynamic incentives and heterogeneity in preferences. We show evidence suggesting that patient decisions on the deceased donor kidney wait list respond to dynamic incentives. Therefore, an empirical approach to dynamic mechanism design is an essential complement to mechanism design theory in dynamic environments.

scholarly journals Reinforcement Learning - A Technical Introduction

2019 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Elmar Diederichs
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Learning Problem ◽  
Open Problems ◽  
Recent Developments ◽  
Directed Learning ◽  
Abstract Class ◽  
Algorithmic Approaches

Reinforcement learning provides a cognitive science perspective to behavior and sequential decision making provided that RL-algorithms introduce a computational concept of agency to the learning problem. Hence it addresses an abstract class of problems that can be characterized as follows: An algorithm confronted with information from an unknown environment is supposed to find stepwise an optimal way to behave based only on some sparse, delayed or noisy feedback from some environment, that changes according to the algorithm's behavior. Hence reinforcement learning offers an abstraction to the problem of goal-directed learning from interaction. The paper offers an opintionated introduction in the algorithmic advantages and drawbacks of several algorithmic approaches such that one can understand recent developments and open problems in reinforcement learning.

scholarly journals Intelligent and Learning Agents: Four Investigations

2021 ◽  
Author(s):  
Shivaram Kalyanakrishnan
Keyword(s):  
Decision Making ◽  
Recent Work ◽  
Intelligent Agents ◽  
Early Career ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Analysis And Design ◽  
Learning Agents ◽  
Early Career Researcher

My research is driven by my curiosity about the nature of intelligence. Of the several aspects that characterise the behaviour of intelligent agents, I primarily study sequential decision making, learning, and exploration. My interests also extend to broader questions on the effects of AI on life and society. In this paper, I present four distinct investigations drawn from my recent work, which range from theoretical to applied, and which involve both analysis and design. I also share my outlook as an early-career researcher.

scholarly journals Propuesta de una arquitectura para un Smart Campus Universitario

2020 ◽  
pp. 21-27
Author(s):  
José María CRUZ-PARADA ◽  
Víctor Manuel ZAMUDIO-RODRIGUEZ ◽  
Carlos LINO-RAMÍREZ ◽  
David Asael GUTIERREZ-HERNANDEZ
Keyword(s):  
Decision Making ◽  
Control System ◽  
Data Collection ◽  
Intelligent Agents ◽  
Mobile Application ◽  
University Campus ◽  
Information Analysis ◽  
Smart Campus ◽  
The Creation ◽  
Automated Decision Making

A proposal of an architecture is described for the use of intelligent agents connected to a mobile application and the same time is also linked to a control system that is managed by the institution. In this document the idea is analyzed from its conception, through the elaborated development and the tests and the results that have been carried out. This architecture is planned to be used in the creation of an intelligent university campus with data collection, information analysis and automated decision making.

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