{"title":"基于代理的机制设计——研究预算背景下的有限理性概念","authors":"Iris Lorscheid, M. Meyer","doi":"10.1108/TPM-10-2015-0048","DOIUrl":null,"url":null,"abstract":"Purpose \n \n \n \n \nThis study aims to demonstrate how agent-based simulation (ABS) may provide a computational testbed for mechanism design using concepts of bounded rationality (BR). ABS can be used to systematically derive and formalize different models of BR. This allows us to identify the cognitive preconditions for behavior intended by the mechanism and thereby to derive implications for the design of mechanisms. \n \n \n \n \nDesign/methodology/approach \n \n \n \n \nBased on an analysis of the requirements of the decision context, the authors describe a systematic way of incorporating different BR concepts into an agent learning model. The approach is illustrated by analyzing an incentive scheme suggested for truthful reporting in budgeting contexts, which is an adapted Groves mechanism scheme. \n \n \n \n \nFindings \n \n \n \n \nThe study describes systematic ways in which to derive BR agents for research questions where behavioral aspects might matter. The authors show that BR concepts may lead to other outcomes than the intended truth-inducing effect. A modification of the mechanism to more distinguishable levels of payments improves the results in terms of the intended effect. \n \n \n \n \nResearch limitations/implications \n \n \n \n \nThe presented BR concepts as simulated by agent models cannot model human behavior in its full complexity. The simplification of complex human behavior is a useful analytical construct for the controlled analysis of a few aspects and an understanding of the potential consequences of those aspects of human behavior for mechanism design. \n \n \n \n \nOriginality/value \n \n \n \n \nThe paper specifies the idea of a computational testbed for mechanism design based on BR concepts. Beyond this, a systematic and stepwise approach is shown to formalize bounded rational behavior by agents based on a requirements analysis, including benchmark models for the comparison and evaluation of BR concepts.","PeriodicalId":46084,"journal":{"name":"Team Performance Management","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/TPM-10-2015-0048","citationCount":"2","resultStr":"{\"title\":\"Agent-based mechanism design – investigating bounded rationality concepts in a budgeting context\",\"authors\":\"Iris Lorscheid, M. Meyer\",\"doi\":\"10.1108/TPM-10-2015-0048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose \\n \\n \\n \\n \\nThis study aims to demonstrate how agent-based simulation (ABS) may provide a computational testbed for mechanism design using concepts of bounded rationality (BR). ABS can be used to systematically derive and formalize different models of BR. This allows us to identify the cognitive preconditions for behavior intended by the mechanism and thereby to derive implications for the design of mechanisms. \\n \\n \\n \\n \\nDesign/methodology/approach \\n \\n \\n \\n \\nBased on an analysis of the requirements of the decision context, the authors describe a systematic way of incorporating different BR concepts into an agent learning model. The approach is illustrated by analyzing an incentive scheme suggested for truthful reporting in budgeting contexts, which is an adapted Groves mechanism scheme. \\n \\n \\n \\n \\nFindings \\n \\n \\n \\n \\nThe study describes systematic ways in which to derive BR agents for research questions where behavioral aspects might matter. The authors show that BR concepts may lead to other outcomes than the intended truth-inducing effect. A modification of the mechanism to more distinguishable levels of payments improves the results in terms of the intended effect. \\n \\n \\n \\n \\nResearch limitations/implications \\n \\n \\n \\n \\nThe presented BR concepts as simulated by agent models cannot model human behavior in its full complexity. The simplification of complex human behavior is a useful analytical construct for the controlled analysis of a few aspects and an understanding of the potential consequences of those aspects of human behavior for mechanism design. \\n \\n \\n \\n \\nOriginality/value \\n \\n \\n \\n \\nThe paper specifies the idea of a computational testbed for mechanism design based on BR concepts. Beyond this, a systematic and stepwise approach is shown to formalize bounded rational behavior by agents based on a requirements analysis, including benchmark models for the comparison and evaluation of BR concepts.\",\"PeriodicalId\":46084,\"journal\":{\"name\":\"Team Performance Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2017-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1108/TPM-10-2015-0048\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Team Performance Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/TPM-10-2015-0048\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Team Performance Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/TPM-10-2015-0048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
Agent-based mechanism design – investigating bounded rationality concepts in a budgeting context
Purpose
This study aims to demonstrate how agent-based simulation (ABS) may provide a computational testbed for mechanism design using concepts of bounded rationality (BR). ABS can be used to systematically derive and formalize different models of BR. This allows us to identify the cognitive preconditions for behavior intended by the mechanism and thereby to derive implications for the design of mechanisms.
Design/methodology/approach
Based on an analysis of the requirements of the decision context, the authors describe a systematic way of incorporating different BR concepts into an agent learning model. The approach is illustrated by analyzing an incentive scheme suggested for truthful reporting in budgeting contexts, which is an adapted Groves mechanism scheme.
Findings
The study describes systematic ways in which to derive BR agents for research questions where behavioral aspects might matter. The authors show that BR concepts may lead to other outcomes than the intended truth-inducing effect. A modification of the mechanism to more distinguishable levels of payments improves the results in terms of the intended effect.
Research limitations/implications
The presented BR concepts as simulated by agent models cannot model human behavior in its full complexity. The simplification of complex human behavior is a useful analytical construct for the controlled analysis of a few aspects and an understanding of the potential consequences of those aspects of human behavior for mechanism design.
Originality/value
The paper specifies the idea of a computational testbed for mechanism design based on BR concepts. Beyond this, a systematic and stepwise approach is shown to formalize bounded rational behavior by agents based on a requirements analysis, including benchmark models for the comparison and evaluation of BR concepts.
期刊介绍:
This international journal contributes to the successful implementation and development of work teams and team-based organizations by providing a forum for sharing experience and learning to stimulate thought and transfer of ideas. It seeks to bridge the gap between research and practice by publishing articles where the claims are evidence-based and the conclusions have practical value. Effective teams form the heart of every successful organization. But team management is one of the hardest challenges faced by managers.