Editing Game theory (section)

==General and applied uses==
As a method of [[applied mathematics]], game theory has been used to study a wide variety of human and animal behaviors. It was initially developed in [[economics]] to understand a large collection of economic behaviors, including behaviors of firms, markets, and consumers. The first use of game-theoretic analysis was by [[Antoine Augustin Cournot]] in 1838 with his solution of the [[Cournot competition|Cournot duopoly]]. The use of game theory in the social sciences has expanded, and game theory has been applied to political, sociological, and psychological behaviors as well.<ref name="Larson">{{cite journal |last1=Larson |first1=Jennifer M. |title=Networks of Conflict and Cooperation |journal=Annual Review of Political Science |date=11 May 2021 |volume=24 |issue=1 |pages=89–107 |doi=10.1146/annurev-polisci-041719-102523 |doi-access=free }}</ref>

Although pre-twentieth-century [[Natural history|naturalists]] such as [[Charles Darwin]] made game-theoretic kinds of statements, the use of game-theoretic analysis in biology began with [[Ronald Fisher]]'s studies of animal behavior during the 1930s. This work predates the name "game theory", but it shares many important features with this field. The developments in economics were later applied to biology largely by John Maynard Smith in his 1982 book ''[[Evolution and the Theory of Games]]''.<ref>{{Cite journal |last=Friedman |first=Daniel |date=1998 |title=On economic applications of evolutionary game theory |url=https://leeps.ucsc.edu/media/papers/EconAppEvolGameTheory3-1-98.pdf |archive-url=https://web.archive.org/web/20140211210314/http://leeps.ucsc.edu/media/papers/EconAppEvolGameTheory3-1-98.pdf |archive-date=2014-02-11 |url-status=live |journal=Journal of Evolutionary Economics |volume=8 |pages=14–53}}</ref>

In addition to being used to describe, predict, and explain behavior, game theory has also been used to develop theories of ethical or normative behavior and to [[Decision theory#Normative and descriptive decision theory|prescribe]] such behavior.<ref name="Camerer2003">{{cite book |author-link=Colin F. Camerer |first=Colin F. |last=Camerer |date=2003 |title=Behavioral Game Theory: Experiments in Strategic Interaction |pages=5–7 |url=http://press.princeton.edu/chapters/i7517.html |chapter=1.1 What Is Game Theory Good For? |archive-url=https://web.archive.org/web/20110514201411/http://press.princeton.edu/chapters/i7517.html |archive-date=14 May 2011}}</ref> In [[economics and philosophy]], scholars have applied game theory to help in the understanding of good or proper behavior. Game-theoretic approaches have also been suggested in the [[philosophy of language]] and [[philosophy of science]].<ref>{{cite journal |last1=Bruin |first1=Boudewijn de |title=Game Theory in Philosophy |journal=Topoi |date=September 2005 |volume=24 |issue=2 |pages=197–208 |doi=10.1007/s11245-005-5055-3 }}</ref> Game-theoretic arguments of this type can be found as far back as [[Plato]].<ref>{{cite encyclopedia |url=http://plato.stanford.edu/archives/spr2008/entries/game-theory/ |title=Game Theory |access-date=21 August 2008 |last=Ross |first=Don |encyclopedia=Stanford Encyclopedia of Philosophy |date=10 March 2006 |publisher=Stanford University |editor-first=Edward N. |editor-last=Zalta }}</ref> An alternative version of game theory, called [[chemical game theory]], represents the player's choices as metaphorical chemical reactant molecules called "knowlecules".<ref>{{cite journal |last1=Velegol |first1=Darrell |last2=Suhey |first2=Paul |last3=Connolly |first3=John |last4=Morrissey |first4=Natalie |last5=Cook |first5=Laura |title=Chemical Game Theory |journal=Industrial & Engineering Chemistry Research |date=17 October 2018 |volume=57 |issue=41 |pages=13593–13607 |doi=10.1021/acs.iecr.8b03835 |s2cid=105204747 }}</ref> &nbsp;Chemical game theory then calculates the outcomes as equilibrium solutions to a system of chemical reactions.

===Description and modeling===
[[File:Centipede game.svg|thumb|upright=1.25|right|A four-stage [[centipede game]]]]
The primary use of game theory is to describe and [[Conceptual model#Economic models|model]] how human populations behave.{{Citation needed|date=November 2019}} Some{{Who|date=July 2012}} scholars believe that by finding the equilibria of games they can predict how actual human populations will behave when confronted with situations analogous to the game being studied. This particular view of game theory has been criticized. It is argued that the assumptions made by game theorists are often violated when applied to real-world situations. Game theorists usually assume players act rationally, but in practice, human rationality and/or behavior often deviates from the model of rationality as used in game theory. Game theorists respond by comparing their assumptions to those used in [[physics]]. Thus while their assumptions do not always hold, they can treat game theory as a reasonable scientific [[Idealization (science philosophy)|ideal]] akin to the models used by [[physicist]]s. However, empirical work has shown that in some classic games, such as the [[centipede game]], [[guess 2/3 of the average]] game, and the [[dictator game]], people regularly do not play Nash equilibria. There is an ongoing debate regarding the importance of these experiments and whether the analysis of the experiments fully captures all aspects of the relevant situation.{{efn|Experimental work in game theory goes by many names, [[experimental economics]], [[behavioral economics]], and [[behavioural game theory]] are several.<ref>{{cite book |first=Colin F. |last=Camerer |date=2003 |title=Behavioral Game Theory: Experiments in Strategic Interaction |url=http://press.princeton.edu/chapters/i7517.html |chapter=Introduction |archive-url=https://web.archive.org/web/20110514201411/http://press.princeton.edu/chapters/i7517.html |archive-date=14 May 2011 |pages=1–25 }}</ref>}}

Some game theorists, following the work of John Maynard Smith and [[George R. Price]], have turned to evolutionary game theory in order to resolve these issues. These models presume either no rationality or [[bounded rationality]] on the part of players. Despite the name, evolutionary game theory does not necessarily presume [[natural selection]] in the biological sense. Evolutionary game theory includes both biological as well as cultural evolution and also models of individual learning (for example, [[fictitious play]] dynamics).

===Prescriptive or normative analysis===
{{Payoff matrix |Name=The prisoner's dilemma | 2L=Cooperate |2R=Defect |1U=Cooperate |UL=-1, −1 |UR=-10, 0 |1D=Defect |DL=0, −10 |DR=-5, −5}}
Some scholars see game theory not as a predictive tool for the behavior of human beings, but as a suggestion for how people ought to behave. Since a strategy, corresponding to a Nash equilibrium of a game constitutes one's [[best response]] to the actions of the other players – provided they are in (the same) Nash equilibrium – playing a strategy that is part of a Nash equilibrium seems appropriate. This normative use of game theory has also come under criticism.<ref>{{cite journal |last1=Kadane |first1=Joseph B. |last2=Larkey |first2=Patrick D. |title=The Confusion of Is and Ought in Game Theoretic Contexts |journal=Management Science |date=December 1983 |volume=29 |issue=12 |pages=1365–1379 |doi=10.1287/mnsc.29.12.1365 }}</ref>

{{Anchor|Economics}}
=== Economics ===<!-- This Anchor tag serves to provide a permanent target for incoming section links. Please do not remove it, nor modify it, except to add another appropriate anchor. If you modify the section title, please anchor the old title. It is always best to anchor an old section header that has been changed so that links to it will not be broken. See [[Template:Anchor]] for details. This template is {{subst:Anchor comment}} -->
Game theory is a major method used in mathematical economics and business for [[Economic model|modeling]] competing behaviors of interacting [[Agent (economics)|agents]].{{efn|At [[JEL classification codes#Mathematical and quantitative methods JEL: C Subcategories|JEL:C7]] of the ''[[Journal of Economic Literature]]'' classification codes.}}<ref>{{cite book |author-link= Robert Aumann |first=Robert J. |last=Aumann |date=2008 |chapter=game theory |title=[[The New Palgrave Dictionary of Economics]] |edition=2nd |chapter-url= http://www.dictionaryofeconomics.com/article?id=pde2008_G000007&edition=current&q=game%20theory&topicid=&result_number=4 |access-date=22 August 2011 |archive-url=https://web.archive.org/web/20110515034120/http://www.dictionaryofeconomics.com/article?id=pde2008_G000007&edition=current&q=game%20theory&topicid=&result_number=4 |archive-date=15 May 2011 |url-status=dead }}</ref><ref>{{cite book |author-link= Martin Shubik |first=Martin |last=Shubik |date=1981 |chapter=Game Theory Models and Methods in Political Economy |editor-link1=Kenneth Arrow |editor-first1=Kenneth |editor-last1=Arrow |editor-first2= Michael |editor-last2=Intriligator |title=Handbook of Mathematical Economics, v.&nbsp;1 |volume=1 |version=1 |pages=285–330 |doi=10.1016/S1573-4382(81)01011-4|isbn=978-0-444-86126-9 }}</ref><ref>{{cite journal |last= Shapiro|first=Carl|author-link=Carl Shapiro|date=Spring 1989|title=The Theory of Business Strategy|journal=The RAND Journal of Economics|volume=20|number=1|pages=125–137|jstor=2555656 |publisher= [[Wiley (publisher)|Wiley]] |pmid= 10296625 }}.</ref> Applications include a wide array of economic phenomena and approaches, such as [[auction]]s, [[bargaining]], [[mergers and acquisitions]] pricing,<ref name="GT-A-E-00">{{cite conference|last1=Agarwal|first1=N.|last2=Zeephongsekul|first2=P.|url=http://www.mssanz.org.au/modsim2011/D6/agarwal.pdf |title=Psychological Pricing in Mergers & Acquisitions using Game Theory|conference=19th International Congress on Modelling and Simulation|conference-url=https://mssanz.org.au/modsim2011/ |location=[[Perth]]|access-date=February 3, 2023|date=December 11–12, 2011}}</ref> [[fair division]], [[duopoly|duopolies]], [[oligopoly|oligopolies]], [[social network]] formation, [[agent-based computational economics]],<ref>{{cite book |doi=10.1016/S1574-0021(05)02016-2 |title=Agent-Based Computational Economics: A Constructive Approach to Economic Theory |series=Handbook of Computational Economics |date=2006 |last1=Tesfatsion |first1=Leigh |volume=2 |pages=831–880 |isbn=978-0-444-51253-6 }}</ref><ref>{{cite book |date=2008 |title=The New Palgrave Dictionary of Economics |chapter=computer science and game theory |author=[[Joseph Y. Halpern]] |chapter-url=http://www.dictionaryofeconomics.com/article?id=pde2008_C000566&edition=current&topicid=&result_number=1 }}</ref> [[general equilibrium]], mechanism design,<ref>{{cite book |date=2008 |title=The New Palgrave Dictionary of Economics |chapter=mechanism design |author-link=Roger B. Myerson |first=Roger B. |last=Myerson |chapter-url=http://www.dictionaryofeconomics.com/article?id=pde2008_M000132&edition=current&q=mechanism%20design&topicid=&result_number=3 |access-date=4 August 2011 |archive-url=https://web.archive.org/web/20111123042038/http://www.dictionaryofeconomics.com/article?id=pde2008_M000132&edition=current&q=mechanism%20design&topicid=&result_number=3 |archive-date=23 November 2011 |url-status=dead }}</ref><ref>{{cite book |date=2008 |title=The New Palgrave Dictionary of Economics |chapter=revelation principle |author-link=Roger B. Myerson |first=Roger B. |last=Myerson |chapter-url=http://www.dictionaryofeconomics.com/article?id=pde2008_R000137&edition=current&q=moral&topicid=&result_number=1 |access-date=4 August 2011 |archive-date=16 May 2013 |archive-url=https://web.archive.org/web/20130516143359/http://www.dictionaryofeconomics.com/article?id=pde2008_R000137&edition=current&q=moral&topicid=&result_number=1 |url-status=live }}</ref><ref>{{cite book |date=2008 |title=The New Palgrave Dictionary of Economics |chapter=computing in mechanism design |first=Tuomas |last=Sandholm |chapter-url=http://www.dictionaryofeconomics.com/article?id=pde2008_C000563&edition=&field=keyword&q=algorithmic%20mechanism%20design&topicid=&result_number=1 |access-date=5 December 2011 |archive-url=https://web.archive.org/web/20111123042038/http://www.dictionaryofeconomics.com/article?id=pde2008_C000563&edition=&field=keyword&q=algorithmic%20mechanism%20design&topicid=&result_number=1 |archive-date=23 November 2011 |url-status=dead }}</ref><ref name= "nisanronen2001">{{cite journal |last1=Nisan |first1=Noam |last2=Ronen |first2=Amir |title=Algorithmic Mechanism Design |journal=Games and Economic Behavior |date=April 2001 |volume=35 |issue=1–2 |pages=166–196 |doi=10.1006/game.1999.0790 }}</ref><ref name="nisan2007">{{cite book|editor-link1=Noam Nisan |editor-first1=Noam |editor-last1=Nisan|editor-last2=Roughgarden|editor-first2=Tim|editor-last3=Tardos|editor-first3= Eva |editor-last4= Vazirani|editor-first4=Vijay V.|year=2007|title=Algorithmic Game Theory |publisher=[[Cambridge University Press]]|isbn=9780521872829|lccn=2007014231}}</ref> and [[voting system]]s;<ref>{{cite book|doi=10.1016/S1574-0005(05)80062-1|title=Chapter 30 Voting procedures|volume=2|pages=1055–1089|series=Handbook of Game Theory with Economic Applications |year= 1994 |last1= Brams|first1=Steven J.|isbn=978-0-444-89427-4}} and {{cite book|doi=10.1016/S1574-0005(05)80063-3|title=Chapter 31 Social choice|volume=2|pages=1091–1125|series=Handbook of Game Theory with Economic Applications|year=1994|last1=Moulin|first1=Hervé|isbn=978-0-444-89427-4}}</ref> and across such broad areas as experimental economics,<ref>{{cite journal |last1=Smith |first1=Vernon L. |title=Game Theory and Experimental Economics: Beginnings and Early Influences |journal=History of Political Economy |date=December 1992 |volume=24 |issue=Supplement |pages=241–282 |doi=10.1215/00182702-24-Supplement-241 }}</ref><ref>{{cite book|doi= 10.1016/B0-08-043076-7/02232-4 |chapter=Experimental Economics|title=International Encyclopedia of the Social & Behavioral Sciences|pages=5100–5108|year=2001|last=Smith|first=Vernon L.|author-link= Vernon L. Smith |isbn= 978-0-08-043076-8}}</ref><ref>{{cite book |editor1-last=Plott |editor1-first=Charles R. |editor2-last=Smith |editor2-first=Vernon L. |title=Handbook of Experimental Economics Results |date=2008 |publisher=Elsevier |isbn=978-0-08-088796-8 }}{{page needed|date=July 2024}}</ref><ref>Vincent P. Crawford (1997). "Theory and Experiment in the Analysis of Strategic Interaction," in ''Advances in Economics and Econometrics: Theory and Applications'', pp. [http://weber.ucsd.edu/~vcrawfor/CrawfordThExp97.pdf 206–242] {{Webarchive|url=https://web.archive.org/web/20120401234518/http://weber.ucsd.edu/~vcrawfor/CrawfordThExp97.pdf |date=1 April 2012 }}. Cambridge. Reprinted in Colin F. Camerer ''et al''., ed. (2003). ''Advances in Behavioral Economics'', Princeton. 1986–2003 papers. [http://press.princeton.edu/titles/8437.html Description] {{Webarchive|url=https://web.archive.org/web/20120118024451/http://press.princeton.edu/titles/8437.html |date=18 January 2012 }}, [https://books.google.com/books?id=sA4jJOjwCW4C&pg=PR7 preview], Princeton, ch. 12</ref><ref>{{cite book|doi=10.1016/S1574-0005(02)03025-4|chapter=Chapter 62 Game theory and experimental gaming |title=Handbook of Game Theory with Economic Applications Volume 3|volume=3|pages=2327–2351|year=2002|last1=Shubik|first1=Martin|isbn=978-0-444-89428-1}}</ref> [[Behavioral game theory|behavioral economics]],<ref>{{cite book |date=2008 |title=The New Palgrave Dictionary of Economics }}[[Faruk Gul]]. "behavioural economics and game theory." [http://www.dictionaryofeconomics.com/article?id=pde2008_G000210&q=Behavioral%20economics%20&topicid=&result_number=2 Abstract.] {{Webarchive|url=https://web.archive.org/web/20170807112808/http://www.dictionaryofeconomics.com/article?id=pde2008_G000210&q=Behavioral%20economics%20&topicid=&result_number=2 |date=7 August 2017 }}</ref><ref>{{cite book |date=2008 |title=The New Palgrave Dictionary of Economics |chapter=behavioral game theory |author-link=Colin F. Camerer |first=Colin F. |last=Camerer |chapter-url=http://www.dictionaryofeconomics.com/article?id=pde2008_B000302&q=Behavioral%20economics%20&topicid=&result_number=13 |access-date=4 August 2011 |archive-url=https://web.archive.org/web/20111123034346/http://www.dictionaryofeconomics.com/article?id=pde2008_B000302&q=Behavioral%20economics%20&topicid=&result_number=13 |archive-date=23 November 2011 |url-status=dead }}</ref><ref>{{cite journal |author-link=Colin F. Camerer |first=Colin F. |last=Camerer |date=1997 |title=Progress in Behavioral Game Theory |journal=Journal of Economic Perspectives |volume=11 |issue=4 |pages=172 |doi=10.1257/jep.11.4.167 |url=https://resolver.caltech.edu/CaltechAUTHORS:20110211-074641387 }}</ref><ref>{{cite book |author-link=Colin F. Camerer |first=Colin F. |last=Camerer |date=2003 |title=Behavioral Game Theory |publisher= Princeton}} [http://press.princeton.edu/chapters/i7517.html Description] {{Webarchive|url=https://web.archive.org/web/20110514201411/http://press.princeton.edu/chapters/i7517.html |date= 14 May 2011 }}, [https://books.google.com/books?id=cr_Xg7cRvdcC&pg=PR7 preview] {{Webarchive|url=https://web.archive.org/web/20230326164811/https://books.google.com/books?id=cr_Xg7cRvdcC&pg=PR7 |date=26 March 2023 }} ([ctrl]+), and ch. 1 [http://press.princeton.edu/chapters/i7517.pdf link] {{Webarchive|url=https://web.archive.org/web/20130704185133/http://press.princeton.edu/chapters/i7517.pdf |date=4 July 2013 }}.</ref><ref>{{cite book |editor1-last=Camerer |editor1-first=Colin F. |editor2-last=Loewenstein |editor2-first=George |editor3-last=Rabin |editor3-first=Matthew |title=Advances in Behavioral Economics |date=2011 |publisher=Princeton University Press |isbn=978-1-4008-2911-8 }}{{page needed|date=July 2024}}</ref><ref>{{cite journal |first=Drew |last=Fudenberg |author-link=Drew Fudenberg |date=2006 |title=Advancing Beyond Advances in Behavioral Economics |journal=Journal of Economic Literature |volume=44 |issue=3 |pages=694–711 |doi=10.1257/jel.44.3.694 |jstor=30032349 |s2cid=3490729 |url=http://nrs.harvard.edu/urn-3:HUL.InstRepos:3208222 }}</ref> [[information economics]],<ref name=r7a>{{cite book |first=Eric |last=Rasmusen |date=2007 |title=Games and Information |publisher= Wiley |isbn=978-1-4051-3666-2 |edition=4th |url=https://books.google.com/books?id=5XEMuJwnBmUC&pg=PR5}}</ref><ref name=r7b>{{cite book |last1=Kreps |first1=David M. |author-link=David M. Kreps |title=Game Theory and Economic Modelling |date=1990 |publisher=Oxford University Press |isbn=978-0-19-828381-2 |doi=10.1093/0198283814.001.0001 }}{{page needed|date=July 2024}}</ref><ref name=r7c>{{cite book |editor1-last=Aumann |editor1-first=R. J. |editor2-last=Hart |editor2-first=S. |title=Handbook of Game Theory with Economic Applications |date=1992 |publisher=Elsevier |isbn=978-0-444-89427-4 }}{{page needed|date=July 2024}}</ref><ref name=r7d>{{cite book|doi=10.1016/S1574-0005(02)03006-0|chapter=Chapter 43 Incomplete information |title=Handbook of Game Theory with Economic Applications Volume 3|volume=3|pages=1665–1686|year=2002|last1=Aumann |first1=Robert J.|last2=Heifetz|first2=Aviad|isbn= 978-0-444-89428-1}}</ref> [[industrial organization]],<ref>{{cite book |author-link=Jean Tirole |first=Jean |last=Tirole |date=1988 |title=The Theory of Industrial Organization |publisher=MIT Press}} [https://archive.org/details/theoryofindustri00jean Description] and chapter-preview links, pp. [https://archive.org/details/theoryofindustri00jean vii–ix], "General Organization," pp. [https://archive.org/details/theoryofindustri00jean/page/5 5–6], and "Non-Cooperative Game Theory: A User's Guide Manual,' " ch. 11, pp. [https://archive.org/details/theoryofindustri00jean/page/423 423–59].</ref><ref>{{cite book |doi=10.1016/S1574-0005(02)03012-6 |chapter=Game theory and industrial organization |title=Handbook of Game Theory with Economic Applications Volume 3 |date=2002 |last1=Bagwell |first1=Kyle |last2=Wolinsky |first2=Asher |volume=3 |pages=1851–1895 |isbn=978-0-444-89428-1 }}</ref><ref>{{cite journal |last1=Fels |first1=E. M. |title=Review of Strategy and Market Structure: Competition, Oligopoly, and the Theory of Games |journal=Weltwirtschaftliches Archiv |date=1961 |volume=87 |pages=12–14 |jstor=40434883 }}</ref><ref>{{cite journal |last1=Reid |first1=Gavin C. |title=Review of Market Structure and Behavior |journal=The Economic Journal |date=1982 |volume=92 |issue=365 |pages=200–202 |doi=10.2307/2232276 |jstor=2232276 }}</ref> and [[political economy]].<ref>Martin Shubik (1981). "Game Theory Models and Methods in Political Economy," in ''Handbook of Mathematical Economics'', v.&nbsp;1, pp. 285–330 {{doi|10.1016/S1573-4382(81)01011-4}}.</ref><ref>Martin Shubik (1987). ''A Game-Theoretic Approach to Political Economy''. MIT Press. [http://mitpress.mit.edu/catalog/item/default.asp?tid=5086&ttype=2 Description]. {{webarchive |url=https://web.archive.org/web/20110629151809/http://mitpress.mit.edu/catalog/item/default.asp?tid=5086&ttype=2 |date=29 June 2011 }}</ref><ref>Martin Shubik (1978). "Game Theory: Economic Applications," in W. Kruskal and J.M. Tanur, ed., ''International Encyclopedia of Statistics'', v.&nbsp;2, pp.&nbsp;372–78.</ref><ref name="r7c"/>

This research usually focuses on particular sets of strategies known as [[Solution concept|"solution concepts" or "equilibria"]]. A common assumption is that players act rationally. In non-cooperative games, the most famous of these is the Nash equilibrium. A set of strategies is a Nash equilibrium if each represents a best response to the other strategies. If all the players are playing the strategies in a Nash equilibrium, they have no unilateral incentive to deviate, since their strategy is the best they can do given what others are doing.<ref name="GT-F-R-09">{{cite web|url=http://www.insead.edu/facultyresearch/research/doc.cfm?did=46503 |title=Game-theoretic model to examine the two tradeoffs in the acquisition of information for a careful balancing act |archive-url=https://web.archive.org/web/20130524231021/http://www.insead.edu/facultyresearch/research/doc.cfm?did=46503 |archive-date=24 May 2013 |publisher=[[INSEAD]] |first=Markus |last=Christen |date=1 July 1998 |url-status=dead |access-date=1 July 2012 }}</ref><ref name="GT-F-R-10">{{cite web |url=http://www.europeanfinancialreview.com/?p=4645 |url-status=dead |title=Options Games: Balancing the trade-off between flexibility and commitment |archive-url=https://web.archive.org/web/20130620053305/http://www.europeanfinancialreview.com/?p=4645 |archive-date=20 June 2013 |website=The European Financial Review |date=15 February 2012 |access-date=2013-01-03 |first1=Benoît |last1=Chevalier-Roignant |first2=Lenos |last2=Trigeorgis }}</ref>

The payoffs of the game are generally taken to represent the [[utility]] of individual players.

A prototypical paper on game theory in economics begins by presenting a game that is an abstraction of a particular economic situation. One or more solution concepts are chosen, and the author demonstrates which strategy sets in the presented game are equilibria of the appropriate type. Economists and business professors suggest two primary uses (noted above): ''descriptive'' and ''[[Decision theory#Normative and descriptive decision theory|prescriptive]]''.<ref name="Camerer2003" />

==== Managerial economics ====
Game theory also has an extensive use in a specific branch or stream of economics – [[Managerial economics|Managerial Economics]]. One important usage of it in the field of managerial economics is in analyzing strategic interactions between firms.<ref>{{cite book |doi=10.1017/CBO9780511810534.015 |chapter=Game theory |title=Managerial Economics |date=2005 |pages=331–381 |isbn=978-0-521-81993-0 |first1=Nick |last1=Wilkinson }}</ref> For example, firms may be competing in a market with limited resources, and game theory can help managers understand how their decisions impact their competitors and the overall market outcomes. Game theory can also be used to analyze cooperation between firms, such as in forming strategic alliances or joint ventures. Another use of game theory in managerial economics is in analyzing pricing strategies. For example, firms may use game theory to determine the optimal [[pricing strategy]] based on how they expect their competitors to respond to their pricing decisions. Overall, game theory serves as a useful tool for analyzing strategic interactions and decision making in the context of managerial economics.

=== Business ===
The [[Chartered Institute of Procurement & Supply]] (CIPS) promotes knowledge and use of game theory within the context of business [[procurement]].<ref>{{Cite web |date=2020-11-27 |title=CIPS and TWS Partners promote game theory on the global stage |url=https://www.cips.org/who-we-are/news/cips-and-tws-partners-promote-game-theory-on-the-global-stage/ |access-date=2023-04-20 |archive-url=https://web.archive.org/web/20201127230832/https://www.cips.org/who-we-are/news/cips-and-tws-partners-promote-game-theory-on-the-global-stage/ |archive-date=27 November 2020 }}</ref> CIPS and TWS Partners have conducted a series of surveys designed to explore the understanding, awareness and application of game theory among [[procurement]] professionals. Some of the main findings in their third annual survey (2019) include:
*application of game theory to procurement activity has increased – at the time it was at 19% across all survey respondents
*65% of participants predict that use of game theory applications will grow
*70% of respondents say that they have "only a basic or a below basic understanding" of game theory
*20% of participants had undertaken [[on-the-job training]] in game theory
*50% of respondents said that new or improved software solutions were desirable
*90% of respondents said that they do not have the software they need for their work.<ref>CIPS (2021), [https://www.cips.org/knowledge/procurement-topics-and-skills/game-theory/game-theory/#tabs-3 Game Theory] {{Webarchive|url=https://web.archive.org/web/20210411050812/https://www.cips.org/knowledge/procurement-topics-and-skills/game-theory/game-theory/#tabs-3 |date=11 April 2021 }}, CIPS in conjunction with TWS Partners, accessed 11 April 2021</ref>

===Project management===

Sensible decision-making is critical for the success of projects.  In project management, game theory is used to model the decision-making process of players, such as investors, project managers, contractors, sub-contractors, governments and customers.  Quite often, these players have competing interests, and sometimes their interests are directly detrimental to other players, making project management scenarios well-suited to be modeled by game theory.

Piraveenan (2019)<ref name="Piraveenan 2019">{{cite journal |last1=Piraveenan |first1=Mahendra |title=Applications of Game Theory in Project Management: A Structured Review and Analysis |journal=Mathematics |date=2019 |volume=7 |issue=9 |page=858 |doi=10.3390/math7090858 |doi-access=free }}</ref> in his review provides several examples where game theory is used to model project management scenarios. For instance, an investor typically has several investment options, and each option will likely result in a different project, and thus one of the investment options has to be chosen before the project charter can be produced. Similarly, any large project involving subcontractors, for instance, a construction project, has a complex interplay between the main contractor (the project manager) and subcontractors, or among the subcontractors themselves, which typically has several decision points. For example, if there is an ambiguity in the contract between the contractor and subcontractor, each must decide how hard to push their case without jeopardizing the whole project, and thus their own stake in it. Similarly, when projects from competing organizations are launched, the marketing personnel have to decide what is the best timing and strategy to market the project, or its resultant product or service, so that it can gain maximum traction in the face of competition. In each of these scenarios, the required decisions depend on the decisions of other players who, in some way, have competing interests to the interests of the decision-maker, and thus can ideally be modeled using game theory.

Piraveenan<ref name="Piraveenan 2019" /> summarizes that two-player games are predominantly used to model project management scenarios, and based on the identity of these players, five distinct types of games are used in project management.

* Government-sector–private-sector games (games that model [[public–private partnership]]s)
* Contractor–contractor games
* Contractor–subcontractor games
* Subcontractor–subcontractor games
* Games involving other players

In terms of types of games, both cooperative as well as non-cooperative, normal-form as well as extensive-form, and zero-sum as well as non-zero-sum  are used to model various project management scenarios.

===Political science===
{{Conflict resolution sidebar}}
The application of game theory to [[political science]] is focused in the overlapping areas of [[fair division]], [[political economy]], [[public choice]], [[war's inefficiency puzzle|war bargaining]], [[positive political theory]], and [[social choice theory]]. In each of these areas, researchers have developed game-theoretic models in which the players are often voters, states, special interest groups, and politicians.<ref>{{Cite web |title=What game theory tells us about politics and society |url=https://news.mit.edu/2018/game-theory-politics-alexander-wolitzky-1204 |access-date=2023-04-23 |website=MIT News {{!}} Massachusetts Institute of Technology |date=4 December 2018 |archive-date=23 April 2023 |archive-url=https://web.archive.org/web/20230423144157/https://news.mit.edu/2018/game-theory-politics-alexander-wolitzky-1204 |url-status=live }}</ref>

Early examples of game theory applied to political science are provided by [[Anthony Downs]]. In his 1957 book ''[[An Economic Theory of Democracy]]'',{{sfnp|Downs|1957}} he applies the [[Hotelling's law|Hotelling firm location model]] to the political process. In the Downsian model, political candidates commit to ideologies on a one-dimensional policy space. Downs first shows how the political candidates will converge to the ideology preferred by the median voter if voters are fully informed, but then argues that voters choose to remain rationally ignorant which allows for candidate divergence. Game theory was applied in 1962 to the [[Cuban Missile Crisis]] during the presidency of John F. Kennedy.<ref>{{cite web |first=Steven J. |last=Brams |author-link=Steven Brams |url=https://plus.maths.org/content/game-theory-and-cuban-missile-crisis |title=Game theory and the Cuban missile crisis |website=Plus Magazine |date=1 January 2001 |access-date=31 January 2016 |archive-date=24 April 2015 |archive-url=https://web.archive.org/web/20150424010528/https://plus.maths.org/content/game-theory-and-cuban-missile-crisis |url-status=live }}</ref>

It has also been proposed that game theory explains the stability of any form of political government.  Taking the simplest case of a monarchy, for example, the king, being only one person, does not and cannot maintain his authority by personally exercising physical control over all or even any significant number of his subjects.  Sovereign control is instead explained by the recognition by each citizen that all other citizens expect each other to view the king (or other established government) as the person whose orders will be followed.  Coordinating communication among citizens to replace the sovereign is effectively barred, since conspiracy to replace the sovereign is generally punishable as a crime.<ref>{{Cite web |title=How game theory explains 'irrational' behavior |url=https://mitsloan.mit.edu/ideas-made-to-matter/how-game-theory-explains-irrational-behavior |access-date=2023-04-23 |website=MIT Sloan |date=5 April 2022 |archive-date=23 April 2023 |archive-url=https://web.archive.org/web/20230423144158/https://mitsloan.mit.edu/ideas-made-to-matter/how-game-theory-explains-irrational-behavior |url-status=live }}</ref>  Thus, in a process that can be modeled by variants of the prisoner's dilemma, during periods of stability no citizen will find it rational to move to replace the sovereign, even if all the citizens know they would be better off if they were all to act collectively.{{citation needed|date=July 2024}}

A game-theoretic explanation for [[democratic peace theory|democratic peace]] is that public and open debate in democracies sends clear and reliable information regarding their intentions to other states. In contrast, it is difficult to know the intentions of nondemocratic leaders, what effect concessions will have, and if promises will be kept. Thus there will be mistrust and unwillingness to make concessions if at least one of the parties in a dispute is a non-democracy.<ref>{{cite journal |last1=Levy |first1=Gilat |last2=Razin |first2=Ronny |title=It Takes Two: An Explanation for the Democratic Peace |journal=Journal of the European Economic Association |date=March 2004 |volume=2 |issue=1 |pages=1–29 |doi=10.1162/154247604323015463 |url=http://eprints.lse.ac.uk/539/ }}</ref>

However, game theory predicts that two countries may still go to war even if their leaders are cognizant of the costs of fighting. War may result from asymmetric information; two countries may have incentives to mis-represent the amount of military resources they have on hand, rendering them unable to settle disputes agreeably without resorting to fighting. Moreover, war may arise because of commitment problems: if two countries wish to settle a dispute via peaceful means, but each wishes to go back on the terms of that settlement, they may have no choice but to resort to warfare. Finally, war may result from issue indivisibilities.<ref>{{Cite journal |last=Fearon |first=James D. |date=1 January 1995 |title=Rationalist Explanations for War |journal=International Organization |volume=49 |issue=3 |pages=379–414 |doi=10.1017/s0020818300033324 |jstor=2706903 |s2cid=38573183 }}</ref>

Game theory could also help predict a nation's responses when there is a new rule or law to be applied to that nation. One example is Peter John Wood's (2013) research looking into what nations could do to help reduce climate change. Wood thought this could be accomplished by making treaties with other nations to reduce [[greenhouse gas emissions]]. However, he concluded that this idea could not work because it would create a prisoner's dilemma for the nations.<ref>{{cite journal |last1=Wood |first1=Peter John |title=Climate change and game theory |journal=Annals of the New York Academy of Sciences |date=February 2011 |volume=1219 |issue=1 |pages=153–170 |doi=10.1111/j.1749-6632.2010.05891.x |pmid=21332497 |bibcode=2011NYASA1219..153W |url=http://ageconsearch.umn.edu/record/95061 }}</ref>

=== Defence science and technology ===

Game theory has been used extensively to model decision-making scenarios relevant to defence applications.<ref name=":3">{{cite journal |last1=Ho |first1=Edwin |last2=Rajagopalan |first2=Arvind |last3=Skvortsov |first3=Alex |last4=Arulampalam |first4=Sanjeev |last5=Piraveenan |first5=Mahendra |title=Game Theory in Defence Applications: A Review |journal=Sensors |date=28 January 2022 |volume=22 |issue=3 |pages=1032 |doi=10.3390/s22031032 |doi-access=free |pmid=35161778 |pmc=8838118 |arxiv=2111.01876 |bibcode=2022Senso..22.1032H }}</ref>  Most studies that has applied game theory in defence settings are concerned with Command and Control Warfare, and can be further classified into studies dealing with (i) Resource Allocation Warfare (ii) Information Warfare (iii) Weapons Control Warfare, and (iv) Adversary Monitoring Warfare.<ref name=":3" />  Many of the problems studied are concerned with sensing and tracking, for example a surface ship trying to track a hostile submarine and the submarine trying to evade being tracked, and the interdependent decision making that takes place with regards to bearing, speed, and the sensor technology activated by both vessels.

The tool,<ref name=":4">{{cite conference |last1=Phetmanee |first1=Surasak |last2=Sevegnani |first2=Michele |last3=Andrei |first3=Oana |title=StEVe: A Rational Verification Tool for Stackelberg Security Games |book-title=Integrated Formal Methods: 19th International Conference, IFM 2024 |date=2024 |pages=267–275 |publisher=Springer-Verlag |location=Manchester, United Kingdom |doi=10.1007/978-3-031-76554-4_15 |url=https://doi.org/10.1007/978-3-031-76554-4_15 }}</ref> for example, automates the transformation of public vulnerability data into models, allowing defenders to synthesize optimal defence strategies through Stackelberg equilibrium analysis. This approach enhances cyber resilience by enabling defenders to anticipate and counteract attackers’ best responses, making game theory increasingly relevant in adversarial cybersecurity environments.

Ho et al. provide a broad summary of game theory applications in defence, highlighting its advantages and limitations across both physical and cyber domains.

=== Biology ===
{{Payoff matrix |Name=The [[Chicken (game)#Hawk-Dove|hawk-dove]] game |2L=Hawk |2R=Dove |1U=Hawk |UL=20, 20 |UR=80, 40 |1D=Dove |DL=40, 80 |DR=60, 60}}
{{main|Evolutionary game theory}}

Unlike those in economics, the payoffs for games in [[biology]] are often interpreted as corresponding to [[Fitness (biology)|fitness]]. In addition, the focus has been less on equilibria that correspond to a notion of rationality and more on ones that would be maintained by evolutionary forces. The best-known equilibrium in biology is known as the ''[[evolutionarily stable strategy]]'' (ESS), first introduced in {{harv|Maynard Smith|Price|1973}}. Although its initial motivation did not involve any of the mental requirements of the Nash equilibrium, every ESS is a Nash equilibrium.

In biology, game theory has been used as a model to understand many different phenomena. It was first used to explain the evolution (and stability) of the approximate 1:1 [[sex ratio]]s. {{harv|Fisher|1930}} suggested that the 1:1 sex ratios are a result of evolutionary forces acting on individuals who could be seen as trying to maximize their number of grandchildren.

Additionally, biologists have used evolutionary game theory and the ESS to explain the emergence of [[animal communication]].{{sfnp|Harper|Maynard Smith|2003}} The analysis of [[signaling games]] and [[Cheap talk|other communication games]] has provided insight into the evolution of communication among animals. For example, the [[mobbing behavior]] of many species, in which a large number of prey animals attack a larger predator, seems to be an example of spontaneous emergent organization. Ants have also been shown to exhibit feed-forward behavior akin to fashion (see [[Paul Ormerod]]'s ''[[Butterfly Economics]]'').

Biologists have used the [[Chicken (game)|game of chicken]] to analyze fighting behavior and territoriality.<ref>{{Cite journal |last1=Maynard Smith |first1=John |author-link=John Maynard Smith |title=The theory of games and the evolution of animal conflicts |doi=10.1016/0022-5193(74)90110-6 |journal=Journal of Theoretical Biology |volume=47 |issue=1 |pages=209–221 |year=1974 |pmid=4459582 |bibcode=1974JThBi..47..209M |url=http://www.dklevine.com/archive/refs4448.pdf }}</ref>

According to Maynard Smith, in the preface to ''Evolution and the Theory of Games'', "paradoxically, it has turned out that game theory is more readily applied to biology than to the field of economic behaviour for which it was originally designed". Evolutionary game theory has been used to explain many seemingly incongruous phenomena in nature.<ref name="stan-egt">{{cite encyclopedia |editor-link=Edward N. Zalta |editor-first=Edward N. |editor-last=Zalta |url=http://plato.stanford.edu/entries/game-evolutionary/ |title=Evolutionary Game Theory |encyclopedia=Stanford Encyclopedia of Philosophy |publisher=Stanford University |access-date=3 January 2013 |date=19 July 2009 |first=J. McKenzie |last=Alexander}}</ref>

One such phenomenon is known as [[Altruism in animals|biological altruism]]. This is a situation in which an organism appears to act in a way that benefits other organisms and is detrimental to itself. This is distinct from traditional notions of altruism because such actions are not conscious, but appear to be evolutionary adaptations to increase overall fitness. Examples can be found in species ranging from vampire bats that regurgitate blood they have obtained from a night's hunting and give it to group members who have failed to feed, to worker bees that care for the queen bee for their entire lives and never mate, to [[vervet monkey]]s that warn group members of a predator's approach, even when it endangers that individual's chance of survival.<ref name="qudzyh">{{cite encyclopedia |editor-first=Edward N. |editor-last=Zalta |url=https://plato.stanford.edu/entries/altruism-biological/ |title=Biological Altruism |encyclopedia=Stanford Encyclopedia of Philosophy |date=3 June 2003 |access-date=3 January 2013 |publisher=Stanford University |editor-link=Edward N. Zalta |first=Samir |last=Okasha}}</ref> All of these actions increase the overall fitness of a group, but occur at a cost to the individual.

Evolutionary game theory explains this altruism with the idea of [[kin selection]]. Altruists discriminate between the individuals they help and favor relatives. [[Kin selection#Hamilton's rule|Hamilton's rule]] explains the evolutionary rationale behind this selection with the equation {{Math|c < b &times; r}}, where the cost {{varserif|c}} to the altruist must be less than the benefit {{varserif|b}} to the recipient multiplied by the coefficient of relatedness {{varserif|r}}. The more closely related two organisms are causes the incidences of altruism to increase because they share many of the same alleles. This means that the altruistic individual, by ensuring that the alleles of its close relative are passed on through survival of its offspring, can forgo the option of having offspring itself because the same number of alleles are passed on. For example, helping a sibling (in diploid animals) has a coefficient of {{frac|1|2}}, because (on average) an individual shares half of the alleles in its sibling's offspring. Ensuring that enough of a sibling's offspring survive to adulthood precludes the necessity of the altruistic individual producing offspring.<ref name="qudzyh" /> The coefficient values depend heavily on the scope of the playing field; for example if the choice of whom to favor includes all genetic living things, not just all relatives, we assume the discrepancy between all humans only accounts for approximately 1% of the diversity in the playing field, a coefficient that was {{frac|1|2}} in the smaller field becomes 0.995. Similarly if it is considered that information other than that of a genetic nature (e.g. epigenetics, religion, science, etc.) persisted through time the playing field becomes larger still, and the discrepancies smaller.

===Computer science and logic===
Game theory has come to play an increasingly important role in [[logic]] and in [[computer science]]. Several logical theories have a basis in [[game semantics]]. In addition, computer scientists have used games to model [[interactive computation]]s. Also, game theory provides a theoretical basis to the field of [[multi-agent system]]s.<ref name="ShohamLeyton-Brown2008">{{cite book |last1=Shoham |first1=Yoav |last2=Leyton-Brown |first2=Kevin |title=Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations |date=2008 |publisher=Cambridge University Press |isbn=978-1-139-47524-2 }}{{page needed|date=July 2024}}</ref>

Separately, game theory has played a role in [[online algorithm]]s; in particular, the [[k-server problem|{{var|k}}-server problem]], which has in the past been referred to as ''games with moving costs'' and ''request-answer games''.{{sfnp|Ben-David|Borodin|Karp|Tardos|1994}} [[Yao's principle]] is a game-theoretic technique for proving [[Upper and lower bounds|lower bounds]] on the [[Analysis of algorithms|computational complexity]] of [[randomized algorithm]]s, especially online algorithms.

The emergence of the Internet has motivated the development of algorithms for finding equilibria in games, markets, computational auctions, peer-to-peer systems, and security and information markets. [[Algorithmic game theory]]<ref name="nisan2007"/> and within it [[algorithmic mechanism design]]<ref name="nisanronen2001"/> combine computational [[algorithm design]] and analysis of [[complex system]]s with economic theory.<ref>{{cite book |author-link=Joseph Y. Halpern |first=Joseph Y. |last=Halpern |date=2008 |chapter=Computer science and game theory |title=The New Palgrave Dictionary of Economics |edition=2nd |url=http://www.dictionaryofeconomics.com/article?id=pde2008_C000566&edition=current&topicid=&result_number=1}}</ref><ref>{{cite journal |last1=Shoham |first1=Yoav |title=Computer science and game theory |journal=Communications of the ACM |date=August 2008 |volume=51 |issue=8 |pages=74–79 |doi=10.1145/1378704.1378721 }}</ref><ref>{{cite journal|first1=Amy |last1=Littman |author-link2=Michael L. Littman |first2=Michael L. |year=2007 |title=Introduction to the Special Issue on Learning and Computational Game Theory |journal=Machine Learning |volume=67 |issue=1–2 |pages=3–6 |doi=10.1007/s10994-007-0770-1 |last2=Littman|s2cid=22635389 |doi-access=free }}</ref>

Game theory has multiple applications in the field of artificial intelligence and machine learning. It is often used in developing autonomous systems that can make complex decisions in uncertain environment.<ref>{{cite journal |last1=Hanley |first1=John T. |date=14 December 2021 |title=GAMES, game theory and artificial intelligence |journal=Journal of Defense Analytics and Logistics |volume=5 |issue=2 |pages=114–130 |doi=10.1108/JDAL-10-2021-0011 |doi-access=free}}</ref> Some other areas of application of game theory in AI/ML context are as follows - multi-agent system formation, reinforcement learning,<ref>{{cite book |last1=Albrecht |first1=Stefano V. |title=Multi-Agent Reinforcement Learning: Foundations and Modern Approaches |last2=Christianos |first2=Filippos |last3=Schäfer |first3=Lukas |date=2024 |publisher=MIT Press |isbn=978-0-262-04937-5}}{{page needed|date=July 2024}}</ref> mechanism design etc.<ref>{{cite news |last1=Parashar |first1=Nilesh |date=15 August 2022 |title=What is Game Theory in AI? |url=https://medium.com/@niitwork0921/what-is-game-theory-in-ai-6b7c4c383f03 |work=Medium}}</ref> By using game theory to model the behavior of other agents and anticipate their actions, AI/ML systems can make better decisions and operate more effectively.<ref>{{cite journal |last1=Hazra |first1=Tanmoy |last2=Anjaria |first2=Kushal |date=March 2022 |title=Applications of game theory in deep learning: a survey |journal=Multimedia Tools and Applications |volume=81 |issue=6 |pages=8963–8994 |doi=10.1007/s11042-022-12153-2 |pmc=9039031 |pmid=35496996}}</ref>

===Philosophy===
{{Payoff matrix |Name=Stag hunt | 2L=Stag |2R=Hare |1U=Stag |UL=3, 3 |UR=0, 2 |1D=Hare |DL=2, 0 |DR=2, 2}}
Game theory has been put to several uses in [[philosophy]]. Responding to two papers by {{harvard citations|txt=yes|first=W.V.O.|last=Quine|author1-link=Willard Van Orman Quine|year=1960|year2=1967}}, {{Harvtxt|Lewis|1969}} used game theory to develop a philosophical account of [[Convention (norm)|convention]]. In so doing, he provided the first analysis of [[Common knowledge (logic)|common knowledge]] and employed it in analyzing play in [[coordination game]]s. In addition, he first suggested that one can understand [[Meaning (semiotics)|meaning]] in terms of [[signaling games]]. This later suggestion has been pursued by several philosophers since Lewis.<ref>{{Harvtxt|Skyrms|1996}}</ref>{{sfnp|Grim|Kokalis|Alai-Tafti|Kilb|2004}}<!--<ref>{{harvard citations |txt=yes |last1=Grim |last2=Kokalis |last3=Alai-Tafti |last4=Kilb |last5=St Denis |year=2004}}.</ref>--> Following {{Harvtxt|Lewis|1969}} game-theoretic account of conventions, Edna Ullmann-Margalit (1977) and [[Cristina Bicchieri|Bicchieri]] (2006) have developed theories of [[social norms]] that define them as Nash equilibria that result from transforming a mixed-motive game into a coordination game.<ref>{{citation |first=E. |last=Ullmann-Margalit |title=The Emergence of Norms |publisher=Oxford University Press |year=1977 |isbn=978-0-19-824411-0 |url=https://archive.org/details/emergenceofnorms0024ullm }}{{page needed|date=July 2024}}</ref><ref>{{citation |first=Cristina |last=Bicchieri |author-link=Cristina Bicchieri |title=The Grammar of Society: the Nature and Dynamics of Social Norms |publisher=Cambridge University Press |year=2006 |isbn=978-0-521-57372-6 }}{{page needed|date=July 2024}}</ref>

Game theory has also challenged philosophers to think in terms of interactive [[epistemology]]: what it means for a collective to have common beliefs or knowledge, and what are the consequences of this knowledge for the social outcomes resulting from the interactions of agents. Philosophers who have worked in this area include Bicchieri (1989, 1993),<ref>{{cite journal|title=Self-Refuting Theories of Strategic Interaction: A Paradox of Common Knowledge |journal=Erkenntnis |volume=30 |issue=1–2 |year=1989 |pages=69–85 |doi=10.1007/BF00184816 |last1=Bicchieri |first1=Cristina |s2cid=120848181 |author-link=Cristina Bicchieri}}</ref><ref>{{Citation |last1=Bicchieri |first1=Cristina |author1-link=Cristina Bicchieri |title=Rationality and Coordination |publisher=[[Cambridge University Press]] |isbn=978-0-521-57444-0 |year=1993}}</ref> [[Brian Skyrms|Skyrms]] (1990),<ref>{{citation |first=Brian |last=Skyrms |author-link=Brian Skyrms |title=The Dynamics of Rational Deliberation |publisher=Harvard University Press |year=1990 |isbn=978-0-674-21885-7}}</ref> and [[Robert Stalnaker|Stalnaker]] (1999).<ref>{{cite journal |last1=Stalnaker |first1=Robert |title=Knowledge, Belief and Counterfactual Reasoning in Games |journal=Economics and Philosophy |date=October 1996 |volume=12 |issue=2 |pages=133–163 |doi=10.1017/S0266267100004132 }}</ref>

The synthesis of game theory with [[ethics]] was championed by [[R. B. Braithwaite]].<ref>{{cite book |last1=Braithwaite |first1=Richard Bevan |title=Theory of Games as a Tool for the Moral Philosopher. An Inaugural Lecture Delivered in Cambridge on 2 December 1954 |date=1955 |publisher=University Press |isbn=978-0-521-11351-9 }}{{page needed|date=July 2024}}</ref> The hope was that rigorous mathematical analysis of game theory might help formalize the more imprecise philosophical discussions. However, this expectation was only materialized to a limited extent.<ref>{{cite journal |last1=Kuhn |first1=Steven T. |title=Reflections on Ethics and Game Theory |journal=Synthese |date=July 2004 |volume=141 |issue=1 |pages=1–44 |doi=10.1023/B:SYNT.0000035846.91195.cb }}</ref>

In [[ethics]], some (most notably David Gauthier, Gregory Kavka, and Jean Hampton) {{Who|date=July 2012}} authors have attempted to pursue [[Thomas Hobbes]]' project of deriving morality from self-interest. Since games like the [[prisoner's dilemma]] present an apparent conflict between morality and self-interest, explaining why cooperation is required by self-interest is an important component of this project. This general strategy is a component of the general [[social contract]] view in [[political philosophy]] (for examples, see {{Harvtxt|Gauthier|1986}} and {{Harvtxt|Kavka |1986}}).{{efn|For a more detailed discussion of the use of game theory in ethics, see the Stanford Encyclopedia of Philosophy's entry [http://plato.stanford.edu/entries/game-ethics/ game theory and ethics].}}

Other authors have attempted to use evolutionary game theory in order to explain the emergence of human attitudes about morality and corresponding animal behaviors. These authors look at several games including the prisoner's dilemma, [[stag hunt]], and the [[Nash bargaining game]] as providing an explanation for the emergence of attitudes about morality (see, e.g., {{harvard citations|txt=yes|last=Skyrms|year=1996|year2=2004}} and {{harvard citations|txt=yes|last1=Sober|last2=Wilson|year=1998}}).

===Epidemiology===
Since the decision to take a vaccine for a particular disease is often made by individuals, who may consider a range of factors and parameters in making this decision (such as the incidence and prevalence of the disease, perceived and real risks associated with contracting the disease,  mortality rate, perceived and real risks associated with vaccination, and financial cost of vaccination), game theory has been used to model and predict vaccination uptake in a society.<ref>{{cite journal |last1=Chang |first1=Sheryl L. |last2=Piraveenan |first2=Mahendra |last3=Pattison |first3=Philippa |last4=Prokopenko |first4=Mikhail |title=Game theoretic modelling of infectious disease dynamics and intervention methods: a review |journal=Journal of Biological Dynamics |date=2020 |volume=14 |issue=1 |pages=57–89 |doi=10.1080/17513758.2020.1720322 |pmid=31996099 |arxiv=1901.04143 |bibcode=2020JBioD..14...57C }}</ref><ref>{{cite news |last1=Roberts |first1=Siobhan |title='The Pandemic Is a Prisoner's Dilemma Game' |url=https://www.nytimes.com/2020/12/20/health/virus-vaccine-game-theory.html |work=The New York Times |date=20 December 2020 }}</ref>