Models and experiments on adaptive decision-making typically consider highly simplified environments that bear little resemblance to the complex, heterogeneous world in which animals (including humans) have evolved. These studies reveal an array of so-called cognitive biases and puzzling features of behaviour that seem irrational in the specific situation presented to the decision-maker. Here we review an emerging body of work that highlights spatiotemporal heterogeneity and autocorrelation as key properties of most real-world environments that may help us understand why these biases evolved. Ecologically rational decision rules adapted to such environments can lead to apparently maladaptive behaviour in artificial experimental settings. We encourage researchers to consider environments with greater complexity to understand better how evolution has shaped our cognitive systems.
By considering agents to be a part of their environment, Orseau and Ring's space-time embedded intelligence [11] is a better fit to the real world than the traditional agent framework. However, a selfmodifying AGI that sees future versions of itself as an ordinary part of the environment may run into problems of self-reference. We show that in one particular model based on formal logic, naive approaches either lead to incorrect reasoning that allows an agent to put off an important task forever (the procrastination paradox), or fail to allow the agent to justify even obviously safe rewrites (the Löbian obstacle). We argue that these problems have relevance beyond our particular formalism, and discuss partial solutions.
Classical game theory treats players as special-a description of a game contains a full, explicit enumeration of all players-even though in the real world, "players" are no more fundamentally special than rocks or clouds. It isn't trivial to find a decision-theoretic foundation for game theory in which an agent's coplayers are a non-distinguished part of the agent's environment. Attempts to model both players and the environment as Turing machines, for example, fail for standard diagonalization reasons.
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