This paper uses previous work by the author, which analysed the collapse of Monarch airlines, in order to elucidate the methods by which game theory is used to analyse the behaviour of individuals and firms in the real world. A critique is offered where any limitations of theory can be found, and potential solutions to mitigate these theoretic limitations are suggested.Section A: How Game Theory Can Be Used To Understand Real-World Economic SituationsGame theory provides a way to analyse situations of strategic interdependence i.e. where the optimal choice or strategy for an actor will depend on what others do. The field has seen a rapid expansion, and its insights have been applied to managerial problems; indeed, Lindstädt and Müller (2010) stated that “game theory can provide timely guidance to managers as they tackle difficult and, sometimes, unprecedented situations”, while Saloner (1991) describes the positive uses of game theoretic analysis in strategic management issues.The author found game theoretic analysis to be effective in understanding the collapse of Monarch airlines. A game was constructed and solved in order to show that the Nash equilibrium outcome was the collapse of Monarch; and that if certain parameters, or ‘rules of the game’ in formal game theoretic language, were changed then the stable, equilibrium outcome would have been different. Concepts from information economics, such as moral hazard, were applicable and were used in order to shine light on the nature of the game being ‘played’; for instance, there were clear agency problems present in the case of Monarch, specifically divergent interests between Monarch and their owners, private investment company Greybull Capital, and this had implications for the assumed information sets in the game.Section B: A Critique of Game Theory as an Effective Tool for Economic ModellingFor game theory to be a valuable tool in exploring real world events, it must have a correct understanding of actors in the real world. What follows is a discussion on the limitations of game theory in realistic economic modelling.Many empirical examples show that experimental results are different from Nash equilibrium outcomes predicted by game theory. A few well-known examples are: the ‘centipede game’ (Rosenthal, 1981; McKelvey and Palfrey 1992; Nagel and Tang, 1998), the ‘dictator game’ (Kahneman et al, 1986; Guala and Mittone, 2010), and various ‘guessing’ games (Nagel, 1995; Duffy and Nagel, 1997).This divergence frequently arises where players must choose either to act in their own self-interest, or to opt for a mutually beneficial strategy. If game theorists want to make their economic models more sophisticated, perhaps they must abandon the model of the Homo economicus. One way to do this is perhaps to incorporate insights from behavioural economics and evolutionary game theory.For instance, evolutionary game theory, pioneered by John Maynard Smith in Evolution and the Theory of Games (1982), does not require players to act or reason in a rational manner, and uses biological insights to explain altruistic behaviour in games, where classical game theory predicts rampant selfishness. The emergent area of work has seen great success in biology, and Friedman (1998, p.15) argues that evolutionary games “have considerable unrealized potential for modeling substantive economic issues”. To illustrate an example where a richer understanding of human behaviour and cognitive process would greatly help, take the concept of utility and estimation of preferences, which is vital to solving problems in game theory.The philosophy behind utilities or ‘payoffs’ in game theory stems from work by Samuelson, who pioneered the concept of revealed preference (1938). His work was highly regarded and lead to consumer preferences being thought of in a more technical and objective way than before. There are distinct problems underlying these assumptions when they are applied to game theory. When ‘payoffs’ are assigned to outcomes in a game, these are usually implied to be of a pecuniary nature, and it is to be assumed that more money is always preferred to less money, a reasonable assumption in and of itself. However, there are players who will respond to things other than money, which causes a divergence between equilibrium predictions and experimental results. Indeed, Ochs and Roth (1989) illustrates that predictions, made under the assumption that monetary payoffs are a good proxy for overall utility payoffs, are not in accordance with observed results in experiments. When this reality is taken into account, several philosophical problems arise: when we say that a payoff represents everything that a player cares about, how do we begin to quantify this? There is no objective or technical way to account for people’s taste, ethics, etc., and when one tries to do so what follows is a tautology: economic agents simply act to maximise their utility, with utility being whatever an economic agent chooses or seeks to maximise. This may lead to mathematical nicety and technical simplicity, but it is not provide a platform for understanding the behaviour of complex and emotional humans. As stated, representing player’s payoffs accurately is important because the methods by which games are solved require values for player’s payoffs for each outcome; if these are ultimately inaccurate then predictions cannot be expected to represent reality.Naturally, we must ask whether, in the context of strategic management, it is necessary or even useful for game theory to accurately reflect reality. Perhaps this is too much of an expectation, particularly in light of the aforementioned limitations, and that game theory should best be utilised as a guide on what firms or economic agents should do if they want to act rationally. Why can’t managers simply learn the art of game theoretic analysis and choose theoretic best responses, even if their rivals don’t? Because this still requires accurate estimations of behaviour, due to the element of strategic interdependence: if you are unsure of the motives and actions of other parties then there is no certain way to calculate your best strategies. ConclusionGame theory can provide useful insights, especially in fields like biology with simple organisms whose behaviours are predictable, but when it is applied to real-world economic questions it often falls short. This is mainly due to inappropriate behavioural assumptions about the motivations and thought processes of economic agents in the real world. Perhaps a synthesis of classical game theory and the findings from psychology and behavioural economics can provide a remedy, if even only partly, to these shortcomings. The awarding of the Nobel prize in Economics to Kahneman in 2002 and Thaler in 2017 might be a signal that there is an appetite among economists to question the core assumptions in the field— if this movement continues to gain traction, game theory may undergo a renaissance.