scholarly journals GAMES, game theory and artificial intelligence

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
John T. Hanley
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Game Theory ◽  
Decision Making ◽  
Design Methodology ◽  
Solution Concepts ◽  
Content Type ◽  
Game Theoretic ◽  
The Subject ◽  
Theoretic Solution

PurposeThe purpose of this paper is to illustrate how game theoretic solution concepts inform what classes of problems will be amenable to artificial intelligence and machine learning (AI/ML), and how to evolve the interaction between human and artificial intelligence.Design/methodology/approachThe approach addresses the development of operational gaming to support planning and decision making. It then provides a succinct summary of game theory for those designing and using games, with an emphasis on information conditions and solution concepts. It addresses how experimentation demonstrates where human decisions differ from game theoretic solution concepts and how games have been used to develop AI/ML. It concludes by suggesting what classes of problems will be amenable to AI/ML, and which will not. It goes on to propose a method for evolving human/artificial intelligence.FindingsGame theoretic solution concepts inform classes of problems where AI/ML 'solutions' will be suspect. The complexity of the subject requires a campaign of learning.Originality/valueThough games have been essential to the development of AI/ML, practitioners have yet to employ game theory to understand its limitations.

Autonomous vehicles: a review of the ethical, social and economic implications of the AI revolution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shweta Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Design Methodology ◽  
Technological Advancement ◽  
Economic Implications ◽  
Content Type ◽  
The Public ◽  
Autonomous Cars ◽  
The Subject ◽  
Practical Implications

PurposeThere are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.Design/methodology/approachMaking use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.FindingsTechnology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.Research limitations/implicationsFrom robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.Practical implicationsFuture shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.Social implicationsThe study will be of interest to researchers, academics and the public in general. It will encourage further thinking.Originality/valueThis is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

The Heuristic Use of Game Theory: Insights for Conflict Resolution

2007 ◽  
Vol 3 (2) ◽  
Author(s):  
Ben D. Mor
Keyword(s):  
Game Theory ◽  
Conflict Resolution ◽  
Bargaining Game ◽  
Solution Concepts ◽  
Negotiated Settlement ◽  
Protracted Conflict ◽  
History Of ◽  
Game Theoretic ◽  
Three Stages ◽  
Theoretic Solution

This article illustrates the heuristic use of game theory by applying it to the analysis of conflict resolution. To this end, we will proceed in three stages. First, we will define a generic bargaining game, which confronts two states that share a history of protracted conflict. Second, we will then introduce a gradual and controlled change in the preferences of the two states for the outcomes that are generated by the bargaining game. Third, for the game series that will be produced, we will apply alternative game-theoretic solution concepts and examine the expected implications of different information conditions. That is, we will establish by means of the theory what the states are expected to do in response to the induced change in their own preferences, in those of the opponent—and in their perception of each other. By modifying these parameters, we will be able to analyze the obstacles that are expected to arise in the peacemaking process and the conditions that are required to attain and stabilize a negotiated settlement.

Machine reengineering: robots and people working smarter together

2017 ◽  
Vol 45 (6) ◽  
pp. 50-54 ◽  
Author(s):  
Prashant Shukla ◽  
H. James Wilson ◽  
Allan Alter ◽  
David Lavieri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Design Methodology ◽  
It Professionals ◽  
Content Type ◽  
Public Agency ◽  
Intelligent Machines ◽  
Intelligent Machine ◽  
Working Together ◽  
Practical Implications

Purpose The authors explore the potential of machine learning, computers employ that an algorithm to sort data, make decisions and then continuously assess and improve their functionality. They suggest that it be used to power a radical redesign of company processes that they call machine reengineering. Design/methodology/approach The authors interpret a survey of more than a thousand corporate public agency IT professionals on their use of artificial intelligence and machine learning. Findings Companies that embrace machine learning find that it adds value to the work product of their employees and provides companies with new capabilities. Practical implications Working together with an intelligent machine, workers become custodians of powerfully smart tools, tools that personalize work to maximize their most productive ways of working. Originality/value A guide to establishing a culture that empowers employees to thrive alongside intelligent machines.

scholarly journals Team Production and Esteem: A Dual Selves Model with Belief-Dependent Preferences

Games ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 33
Author(s):  
Matthias Greiff
Keyword(s):  
Game Theory ◽  
Incomplete Information ◽  
Affective Responses ◽  
Team Production ◽  
Solution Concepts ◽  
Game Theoretic ◽  
Psychological Game Theory ◽  
Expression Of Emotions ◽  
Theoretic Solution ◽  
High Effort

We propose a dual selves model to integrate affective responses and belief-dependent emotions into game theory. We apply our model to team production and model a worker as being composed of a rational self, who chooses effort, and an emotional self, who expresses esteem. Similar to psychological game theory, utilities depend on beliefs, but only indirectly. More concretely, emotions affect utilities, and the expression of emotions depends on updated beliefs. Modeling affective responses as actions chosen by the emotional self allows us to apply standard game-theoretic solution concepts. The model reveals that with incomplete information about abilities, workers only choose high effort if esteem is expressed based on interpersonal comparisons and if the preference for esteem is a status preference.

scholarly journals Who performs better? AVMs vs hedonic models

2020 ◽  
Vol 38 (3) ◽  
pp. 213-225 ◽  
Author(s):  
Agostino Valier
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Design Methodology ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Content Type ◽  
Complete Survey ◽  
The Subject ◽  
Automated Valuation ◽  
Practical Implications

PurposeIn the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.Design/methodology/approachAll tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.FindingsMachine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.Practical implicationsAVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.Originality/valueAccording to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

Sampling-based estimation method for parameter estimation in big data business era

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdul Alim ◽  
Diwakar Shukla
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Confidence Interval ◽  
Design Methodology ◽  
Data Centers ◽  
Alert System ◽  
Present Sample ◽  
Content Type ◽  
Multiple Data Sets

PurposeThis paper aims to present sample-based estimation methodologies to compute the confidence interval for the mean size of the content of material communicated on the digital social media platform in presence of volume, velocity and variety. Confidence interval acts as a tool of machine learning and managerial decision-making for coping up big data.Design/methodology/approachRandom sample-based sampling design methodology is adapted and mean square error is computed on the data set. Confidence intervals are calculated using the simulation over multiple data sets. The smallest length confidence interval is the selection approach for the most efficient in the scenario of big data.FindingsResultants of computations herein help to forecast the future need of web-space at data-centers for anticipation, efficient management, developing a machine learning algorithm for predicting better quality of service to users. Finding supports to develop control limits as an alert system for better use of resources (memory space) at data centers. Suggested methodologies are efficient enough for future prediction in big data setup.Practical implicationsIn IT sector, the startup with the establishment of data centers is the current trend of business. Findings herein may help to develop a forecasting system and alert system for optimal decision-making in the enhancement and share of the business.Originality/valueThe contribution is an original piece of thought, idea and analysis, deriving motivation from references appended.

scholarly journals Category Theoretic Analysis of Photon-Based Decision Making

2018 ◽  
Vol 17 (05) ◽  
pp. 1305-1333 ◽  
Author(s):  
Makoto Naruse ◽  
Song-Ju Kim ◽  
Masashi Aono ◽  
Martin Berthel ◽  
Aurélien Drezet ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Single Photon ◽  
Theoretic Model ◽  
Single Photons ◽  
Modeling And Analysis ◽  
Quantitative Metrics ◽  
The Subject ◽  
Underlying Mechanisms

Decision making is a vital function in the age of machine learning and artificial intelligence; however, its physical realization and theoretical fundamentals are not yet well understood. In our former study, we demonstrated that single photons can be used to make decisions in uncertain, dynamically changing environments. The two-armed bandit problem was successfully solved using the dual probabilistic and particle attributes of single photons. In this study, we present a category theoretic modeling and analysis of single-photon-based decision making, including a quantitative analysis that agrees well with the experimental results. The category theoretic model unveils complex interdependencies of the entities of the subject matter in the most simplified manner, including a dynamically changing environment. In particular, the octahedral structure and the braid structure in triangulated categories provide better understandings and quantitative metrics of the underlying mechanisms for the single-photon decision maker. This study provides insight and a foundation for analyzing more complex and uncertain problems for machine learning and artificial intelligence.

scholarly journals Can machine learning, as a RegTech compliance tool, lighten the regulatory burden for charitable organisations in the United Kingdom?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Charanjit Singh ◽  
Lei Zhao ◽  
Wangwei Lin ◽  
Zhen Ye
Keyword(s):  
Machine Learning ◽  
Design Methodology ◽  
Action Plan ◽  
Regulatory Compliance ◽  
International Regulation ◽  
Content Type ◽  
Regulatory Burden ◽  
The United Kingdom ◽  
The Subject ◽  
The Uk

Purpose Machine learning is having a major impact on banking, law and other organisations. The speed with which this technology is developing to undertake tasks that are not only complex and technical but also time-consuming and that are subject to constantly changing parameters is astounding. The purpose of this paper is to explore the extent to which machine learning can be used as a solution to lighten the compliance and regulatory burden on charitable organisations in the UK; so that they can comply with their regulatory duties and develop a coherent and streamlined action plan in relation to technological investment. Design/methodology/approach The subject is approached through the analysis of data, literature and domestic and international regulation. The first part of the study summarises the extent of current regulatory obligations faced by charities, these are then, in the second part, set against the potential technological solutions provided by machine learning as of July 2021. Findings It is suggested that charities can use machine learning as a smart technological solution to ease the regulatory burden they face in a growing and impactful sector. Originality/value The work is original because it is the first to specifically explore how machine learning as a technological advance can assist charities in meeting the regulatory compliance challenge.

scholarly journals Can artificial intelligence, RegTech and CharityTech provide effective solutions for anti-money laundering and counter-terror financing initiatives in charitable fundraising

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Charanjit Singh ◽  
Wangwei Lin
Keyword(s):  
Artificial Intelligence ◽  
Money Laundering ◽  
Design Methodology ◽  
Regulatory Compliance ◽  
Law Firms ◽  
International Regulation ◽  
Content Type ◽  
Technological Advances ◽  
The Subject ◽  
The Uk

Purpose Artificial intelligence has had a major impact on organisations from Banking through to Law Firms. The rate at which technology has developed in terms of tasks that are complex, technical and time-consuming has been astounding. The purpose of this paper is to explore the solutions that AI, RegTech and CharityTech provide to charities in navigating the vast amount of anti-money laundering and counter-terror finance legislation in the UK; so that they comply with the requirements and mitigate the potential risk they face but also develop a more coherent and streamlined set of actions. Design/methodology/approach The subject is approached through the analysis of data, literature and, domestic and international regulation. The first part of the paper explores the current obligations and risks charities face, these are then, in the second part, set against the examination of potential technological solutions as of August 2020. Findings It is suggested that charities underestimate the importance of the nature and size of the threat posed to them, this is significant, as demonstrated, given the growing size and impact of the sector. Technological solutions are suggested to combat the issues charities face. Originality/value The study is original because it is the first to create the notion of CharityTech and to specifically explore what technological advances can assist charities in meeting the regulatory compliance challenge.

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