UC Irvine

Often in economics, we create models of how an idealized economic agent will behave in a particular setting, finding our predictions using constrained maximization of some objective function. However, in doing so we forget the sum total of the cognitive resources it took for us to find such a solution and we marvel when real humans deviate from our models. We forget than an agent may view a situation differently (they do not know the model) or that they lack the resources to find the solution (they cannot solve the model), but both assumptions are implicit in our models and solution techniques. We overlook that agents will often substitute optimization for heuristics, mental rules of thumb that save on costly mental effort. I present three chapters: in the first, I explore determinants of a particular bias, debt aversion, with the goal of understanding how different settings can manipulate the degree to which we obey the bias. In the second, I look more generally at meta models of effort and delve into heuristics that save on cognitive factors. In the third, I test models dealing with extreme ambiguity aversion in order to test their ability to predict human behavior. I find that as complexity of a task increases, we use less precise heuristic solutions, and that regret minimization can explain our behavior in situations where we cannot learn the true state of nature.