Quelles sont les limites de la théorie des jeux lorsqu’elle est appliquée à l’économie du monde réel ?

Game theory assumes that all players are rational, meaning they will make decisions aimed at maximizing their own utility or benefit. In reality, economic actors often deviate from pure rationality due to various factors: Psychological Influences 1.) Emotions: Emotions such as fear, greed, and anxiety can significantly impact decision-making. For instance, during financial crises, fear can lead to panic selling, deviating from the rational decision to hold assets until markets stabilize. 2.) Cognitive Biases: Cognitive biases like overconfidence, anchoring, and loss aversion affect how individuals process information and make decisions. These biases can lead to systematic deviations from rational behavior.

While we have been progressing as humanity in the discovery that the human being is a being that aims to be rational, or at least that is what we intended since the Enlightenment, in reality it is a being whose decisions are often explained from areas far removed from "economic" reason. This explains why we buy a phone that has a little apple for a value 5 times the value of a phone that does the same thing but does not have a little apple on its back. However, in a competitive environment, decisions reflect "rational" behavior. Discarding game theory seems a bit risky to me. Even animals subjected to similar games offer similar responses.

The 2008 financial crisis underscores the limitations of game theory, particularly the assumption of rational players. Financial institutions and investors engaged in complex transactions with mortgage-backed securities (MBS) and collateralized debt obligations (CDOs), expecting to spread risk and achieve high returns. The crisis highlights the limitations of game theory in real-world economics. Market participants often acted irrationally, influenced by psychological biases and short-term incentives, rather than rational decision-making. This underscores the importance of incorporating behavioral economics, information asymmetries, and market complexities into economic models.

Information is often imperfect and asymmetrically distributed, causing businesses and consumers to operate under uncertainty and make less than optimal decisions. Despite this, the application of game theory remains highly effective. It provides a valuable framework for decision-making, allowing economic agents to anticipate actions, assess risks, and develop more informed strategies even with incomplete data. This analytical approach helps navigate the complexities of the economic landscape, making game theory a powerful tool for optimizing outcomes in real-world scenarios. Thus, while perfect information is rare, game theory's insights significantly enhance strategic decision-making.

Here things get complex. Not only is there no perfect and symmetric information in reality, but many times a player does not know how to read the available information. Or worse still, they do not want to read the available information. On the other hand, in the transforming economy we are experiencing on a global scale, events, data, and phenomena are becoming increasingly ambiguous, meaning they provide less and less certainty about whether an observed phenomenon or data is evidence of one trend or reality or another. "The price of gold is rising" in the 1970s meant one thing (distrust in the global economy), now it could mean a thousand more things and none at the same time.

Game theory models often simplify payoffs to make analysis more straightforward. However, in the real economy, payoffs are multifaceted, including intangible elements like brand reputation and consumer trust, making decision-making more nuanced than game theory might predict. Despite these complexities, decisions are typically based on fundamental outcomes such as revenue, prices, and quantities. Complex payoffs are often considered additional factors that are analyzed after the primary results are assessed. Thus, while real-world payoffs are intricate, basic economic metrics remain the primary focus, with other elements evaluated subsequently.

Game theory models often fail to account for the time constraints affecting real-world decision-making. Economic actors must frequently make rapid decisions under pressure, without the luxury of extensive analysis or the ability to accurately predict others' actions over time. This can lead to shortcuts that diverge from the strategic equilibria anticipated by game theory. However, with sufficient knowledge, businesses can create brief or highly simplified models to assist in making sound decisions quickly, thus leveraging game theory's insights even under time constraints.

Time affects different players differently. For a seller who needs to get rid of their merchandise within a certain time frame (for example, sellers of vegetables or fruits), there will be a propensity to sell at lower prices that don't maximize their utility. This explains the cycles of rises and falls in agricultural prices, for example. If you are using game theory models, consider how time affects each of the players.

The main problem with game theory is the assumption of perfect information. In the absence of perfect information, the assumption of a known distribution of possible outcomes is used. As the great Yogi Berra said: "The future ain't what it used to be."

-Game theory typically focuses on static, one-shot games, whereas many real-world economic situations involve repeated interactions over time. The dynamics of these interactions can lead to different outcomes than those predicted by static game models. -

Game theory is everyone’s favourite unit in undergrad Econ. But it’s best left at Uni. From people being rational, to how the messaging is delivered. The real world can sometimes be the exact opposite. Understanding the theory and then throwing out when observing the real world.

You must consider VUCA - Volatile, Uncertain, Complex, and Ambiguous - the global reality across economic, business, and political scenarios. The VUCA concept emerged from military strategists in Iraq and Afghanistan, where strategic decisions were extremely complex due to: volatility (situations changing abruptly), uncertainty (lack of certainty about future events), complexity (non-linear cause-effect relationships), and ambiguity (facts allowing multiple interpretations, causing doubt and confusion). This demanded constant strategy revisions. The global landscape is similarly VUCA, requiring dynamic, adaptable approaches over rigid linear thinking to navigate volatility, uncertainty, complexity, and ambiguity successfully.

Game theory offers a great deal of insight, but it doesn't always capture the unpredictable nature of real-world markets. Take, for example, the reaction of competitors during a major product launch. You might expect them to make moves that maximize their profits, but instead, they often react in ways that seem irrational—perhaps based on a misreading of market signals or simply to shake things up. This shows that real-world decisions can be influenced by emotions or strategic gambits, something game theory might not fully predict. So, while it's a useful tool for understanding strategic interactions, it's important to recognize its limitations when applying it to actual market behaviors.