We are often faced with situations where we must compete with others to attain a mutually-exclusive outcome.1 These can be significant, such as seeking a job position amongst numerous other applicants. Or they can be more lighthearted, such as winning a game of poker amongst friends. Game theory is the study of how ‘players’ (such as individuals, companies, and nations) interact and determine strategies in structured competition. Understanding our decision making in these social environments can also help us improve the decisions we make daily.2
Rock, Paper, Scissors, Shoot!
A typical example of a competitive environment is Rock-Paper-Scissors (RPS) — the simple game where rock crushes scissors, scissors cuts paper, and paper masks rock. Surprisingly, the game’s simplicity has allowed economists to model various aspects of game theory and elucidate our behavior during repeated competition. RPS also enables behavioral economists to compare our assumptions about human rationality with actual behavior.3
Game theory reasonably assumes that the best way to maximize your chances of winning RPS in a repetitive setting is to be unpredictable. In brief, rational behavior in RPS means completely randomizing your choices.4 However, it is quite unfeasible for the human brain to mentally record the frequency of each choice (rock, paper, or scissors) and play randomly (each choice being played precisely 33.33% of the time). This relates to the concept of bounded rationality, which attributes our suboptimal decisions to limitations in time, information, and mental capacity. As a result, the brain instead relies on heuristics — or mental shortcuts — to minimize cognitive load. The most prominent heuristic in RPS is the win-stay lose-shift heuristic.5,6
The win-stay lose-shift heuristic
The win-stay lose-shift heuristic is precisely what it sounds like. This heuristic describes individuals’ tendencies to stick with the same strategy following a win, and switch to different strategy following a draw or loss. For example, if a player chooses rock and wins, they are more likely to play rock again in the next round; if they lose or tie, then they are more likely to play scissors or paper in the next round.5
This heuristic can be explained from an evolutionary perspective. Individuals are more likely to repeat behavior that is positively reinforced and change behavior that is negatively reinforced.5 While such behavior is fundamental, it is somewhat counterintuitive in competitive environments because it increases the predictability of your next move to your opponent.
Interestingly, one study found that reliance on this heuristic — a deviation from rational decision-making — is more common after losses than wins.3 Looking at how emotion and arousal influence our decision-making can explain this finding.
The battle between System 1 and System 2 in RPS
System 1 and System 2 are terms coined by psychologists Keith Stanovich and Richard West to model the two types of cognitive processing we have. System 2 describes our ability to think/plan rationally, thoroughly, and strategically. System 1, on the other hand, describes our mind’s intuition and impulsive action that is often driven by emotion and arousal.7
In competitive environments, our success often depends on our ability to keep our System 1 in check and act primarily through our System 2.7 RPS has been used as a way to model the conflict between System 1 and System 2, and with neuroeconomics flourishing as an emerging field of study, recent research has investigated the neural foundations of our decision-making while playing RPS.
Lewis Forder and Benjamin Dyson, two researchers at the University of Sussex, discovered that following a winning trial, individuals responded with slower reaction times. On the contrary, individuals responded with faster reaction times following a loss/draw trial with a lack of neural activity. Moreover, they found that there was increased neural modulation of brain regions associated with feedback learning following a winning trial in comparison to a losing trial.8 These findings indicate our reliance on System 2 processes following a win System 1 following a loss.