This paper studies the reinforcement learning of Erev and Roth with foregone payoff information in normal form games: players observe not only the realised payoffs but also the ones which they could have obtained if they had chosen the other actions. We provide conditions under which the reinforcement learning process converges to a mixed action profile at which each action is chosen with a probability proportional to its expected payoff. In pure coordination games, the mixed action profile corresponds to the mixed Nash equilibrium.
{"title":"Reinforcement Learning with Foregone Payoff Information in Normal Form Games","authors":"Naoki Funai","doi":"10.2139/ssrn.3343373","DOIUrl":"https://doi.org/10.2139/ssrn.3343373","url":null,"abstract":"This paper studies the reinforcement learning of Erev and Roth with foregone payoff information in normal form games: players observe not only the realised payoffs but also the ones which they could have obtained if they had chosen the other actions. We provide conditions under which the reinforcement learning process converges to a mixed action profile at which each action is chosen with a probability proportional to its expected payoff. In pure coordination games, the mixed action profile corresponds to the mixed Nash equilibrium.","PeriodicalId":356570,"journal":{"name":"CompSciRN: Problem Solving (Topic)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116059464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital devices and services are becoming cheap and so availability has increased a lot. The extensive use of digital devices has made every individual to ride on rising wave of varied and stored digital information. Huge digital data is residing on standalone storage devices as well as on network and cloud based storage devices. All technologies are vulnerable to security. Risk has also increased because digital information is convenient to generate, economic to accumulate, and practically easy to reproduce. Rule-breakers are part of society and so they are also using the technology with the same pace for crime. Every crime now has necessarily digital dimension. Digital Forensics extracts evidence from digital sources to prove crime in court of law. Expert knowledge required in the all phase of forensic investigation and development of latest tools and benchmarking of tools. Use of existing and evolution of better frameworks and training of incumbents needs experience. Challenges are there in Digital Forensics due to continuous technological advancements, old training techniques, lack of standardization, varied frameworks and no formal mechanism to capture expert experiences. Knowledge Management has wide application in processes, training and formalization. Knowledge Management can be the solution for the issues in Digital Forensics. Knowledge Management techniques such as ontology, content management, knowledge maps, knowledge bases etc. can also be used in Digital Forensics. This paper throws light upon scope of knowledge management in Digital Forensics.
{"title":"Scope of Managing Knowledge in Digital Forensics","authors":"Rakesh M. Verma, P. Bansal","doi":"10.2139/ssrn.3363040","DOIUrl":"https://doi.org/10.2139/ssrn.3363040","url":null,"abstract":"Digital devices and services are becoming cheap and so availability has increased a lot. The extensive use of digital devices has made every individual to ride on rising wave of varied and stored digital information. Huge digital data is residing on standalone storage devices as well as on network and cloud based storage devices. All technologies are vulnerable to security. Risk has also increased because digital information is convenient to generate, economic to accumulate, and practically easy to reproduce. Rule-breakers are part of society and so they are also using the technology with the same pace for crime. Every crime now has necessarily digital dimension. Digital Forensics extracts evidence from digital sources to prove crime in court of law. Expert knowledge required in the all phase of forensic investigation and development of latest tools and benchmarking of tools. Use of existing and evolution of better frameworks and training of incumbents needs experience. Challenges are there in Digital Forensics due to continuous technological advancements, old training techniques, lack of standardization, varied frameworks and no formal mechanism to capture expert experiences. Knowledge Management has wide application in processes, training and formalization. Knowledge Management can be the solution for the issues in Digital Forensics. Knowledge Management techniques such as ontology, content management, knowledge maps, knowledge bases etc. can also be used in Digital Forensics. This paper throws light upon scope of knowledge management in Digital Forensics.","PeriodicalId":356570,"journal":{"name":"CompSciRN: Problem Solving (Topic)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129483933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Validation has been an important issue in using the ABM approach. It has been pointed out that deriving the necessary conditions for reproducing specific macro behavior is difficult due to the functional complexity of ABMs. However, based on the authors’ experience with ABMs, we believe it is possible to define the necessary conditions for reproducing each macro behavior by using the structure of the system to express the input conditions. In the present study, a series of computer experiments are conducted to verify this idea. The study analyzes business cycles and the effect of tax reductions on GDP as examples of fundamental macro behaviors of economic systems. The results indicate that the essential model structures for reproducing business cycles and the effects of tax reduction are credit creation for investment and factors relating to the efficiency of the government’s, household’s, and firms’ expenditures, respectively.
{"title":"Model Structure of Agent-Based Artificial Economic System Responsible for Reproducing Fundamental Economic Behavior of Goods Market","authors":"K. Takashima, Shigeaki Ogibayashi","doi":"10.2139/ssrn.3160061","DOIUrl":"https://doi.org/10.2139/ssrn.3160061","url":null,"abstract":"Validation has been an important issue in using the ABM approach. It has been pointed out that deriving the necessary conditions for reproducing specific macro behavior is difficult due to the functional complexity of ABMs. However, based on the authors’ experience with ABMs, we believe it is possible to define the necessary conditions for reproducing each macro behavior by using the structure of the system to express the input conditions. In the present study, a series of computer experiments are conducted to verify this idea. The study analyzes business cycles and the effect of tax reductions on GDP as examples of fundamental macro behaviors of economic systems. The results indicate that the essential model structures for reproducing business cycles and the effects of tax reduction are credit creation for investment and factors relating to the efficiency of the government’s, household’s, and firms’ expenditures, respectively.","PeriodicalId":356570,"journal":{"name":"CompSciRN: Problem Solving (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121355975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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