Note: The early work of the field (Axelrod 1984) is wrote very approachably. It is a good book which you should read. The notebook is a meagre summary for the busy. The internet is full of many lavish introductions to iterated game theory, and I recommend you try some of them out. Game Theory 101, for example, has many animations and a pedagogic structure.
Here we introduce an abstract model for how cooperation might evolve, or not, amongst a population of animals, especially human animals. The basic version is probably a bit too abstract to serve as a quantitative model for anything real but interesting variants are useful in e.g. qualitatively understanding inequity, culture wars and evolution.
I am actively researching results in causal game theory.
1 Background: Game theory in the Prisoner’s Dilemma
See actual basic game theory post.
2 Iterated Prisoner’s Dilemma
An important field of game theory is the field of iterated games. The paradigmatic one is Iterated Prisoner’s Dilemma (“IPD”). This is the field wherein we extend the model a little to encompass the fact that these isolated one-off prisoner’s dilemmas are not a typical situation. In real life, when we interact in society, we meet with people again and again, and moreover there are a lot of people interacting in diverse situations. It is still a simplified model, but a whole bunch of interesting phenomena arise in the marginally more life-like setup.
- Imagine two friends, Alice and Bob, playing a game repeatedly. In each round, they can either cooperate with each other or betray (defect). The twist is they remember what happened in previous rounds.
- Strategy: This is the plan each player follows in every round. For instance, Alice might adopt the “Always Cooperate” strategy, meaning she decides to cooperate in every round, no matter what Bob does.
3 Population Structure and Interaction Model
- Now, imagine there’s a whole group of people, not just Alice and Bob, playing this game. They might randomly pair up for each round, or maybe they always play against the same few people.
- In some versions of this game, players remember not just past actions but also who they were playing against. So, if Alice played against Bob before, she might change her strategy based on what Bob did last time.
4 Competition and Evolution of Strategies
- In this game, winning isn’t about a single round; it’s about doing well over many rounds. Let’s say Alice’s “Always Cooperate” strategy works well against those who also cooperate, but it might not do well against someone who always defects.
- Strategy Evolution: Imagine a player, Charlie, who notices that “Always Defect” works well initially but then starts losing out to players using “Tit-for-Tat” (a strategy where a player cooperates in the first round and then replicates the opponent’s previous action). Charlie might switch to “Tit-for-Tat” to improve his overall score.
- Over time, the most successful strategies become more common. If “Tit-for-Tat” consistently earns higher scores, more players will start using it.
5 Implications
- This game is a simple way to understand complex real-world interactions. It shows how people (or animals, or even computer programs) might change their behaviour based on past experiences and the behaviour of others around them. It’s not just about winning a game; it’s about how strategies evolve and how cooperation or competition can emerge in a community.
By using these concrete examples, we can see how the Iterated Prisoner’s Dilemma is a powerful tool for understanding the dynamics of strategy, memory, and evolution in both social and biological contexts.
6 Population-Level Behaviours:
Clusters of Cooperation: In a mixed population of different strategies, cooperators can form clusters. If players are more likely to interact with those nearby (like in a spatial IPD), these clusters can protect cooperators from defectors, as they mostly interact with each other.
Cycles of Strategies: Sometimes, you’ll see cycles where different strategies rise and fall in prevalence. For instance, if “Always Defect” becomes common, “Tit-for-Tat” might rise in response, as it can protect itself against defectors. But then, a more forgiving strategy might outcompete “Tit-for-Tat” by not retaliating as harshly, and so on.
7 Dependence on Initial Conditions:
- The outcome of an IPD tournament can be highly sensitive to initial conditions. For example, if the game starts with a majority of defectors, cooperative strategies might struggle to gain a foothold. Conversely, if the game starts with many cooperators, they might establish a cooperative norm that resists invasion by defectors.
- The structure of interactions also matters. If players mostly interact with a fixed set of neighbours, it can lead to different dynamics compared to a model where they interact randomly with the entire population.
8 Unintuitive Dynamics:
Emergence of Cooperation: It might seem counterintuitive, but cooperation can emerge and stabilize even in a competitive environment. The success of strategies like “Tit-for-Tat” shows that cooperation can be a robust strategy, even when facing defectors.
The Shadow of the Future: The importance of future interactions in the IPD is often surprising. The longer the shadow of the future (i.e., the more future interactions players expect to have), the greater the incentive to cooperate. This is because the potential future payoffs from cooperation outweigh the immediate gains from defection.
Complexity from Simplicity: The IPD is based on simple rules, yet it can lead to incredibly complex dynamics. This complexity arising from simplicity can be quite unexpected, showing how basic interactions can lead to rich and varied behavioural patterns.
The dynamics of IPD games at the population level can be complex and often counterintuitive. The initial setup of the game and the interaction structure play crucial roles in determining the outcome, and the emergence of cooperation in a competitive environment highlights the nuanced interplay between individual strategies and collective behaviour.