Psychology of Inductive Reasoning and Belief Revision

In this talk, I will discuss newer insights from psychology and the normative belief revision literature with the goal of a better understanding of human inductive reasoning.

I follow Johnson-Laird's definition and call a reasoning process inductive, if information is added in the reasoning process that is not in the factual premises. For instance, reasoning using analogies is inductive.

A motivating example of inductive reasoning is the formation of expectations for novel business opportunities -like Amazon.com in the perspective of 1998, like Google in the perspective of 2004. This example will be used throughout the talk, also, as the explorations are motivated by the attempt to develop an inductive decision theory for economics.

In inductive reasoning processes, inconsistencies can appear and need to be resolved. I will apply insights from cognitive science and psychology to understand how decision-makers resolve such inconsistencies. To come back to the example, for Amazon, some analysts in 1998 argued that its margins should be higher than margins of large stores like Barnes and Noble, as Amazon is a direct seller with a model like Dell computers. Other analysts argued that Amazon has properties like a physical retailer, and large stores like Barnes and Nobel should generate higher margins as they have higher purchasing power. Essentially, in my work I try to understand how decision-makers (should) deal with such inconsistent inductive explanations.

I will start with a (very rough) overview of the normative belief revision literature, which started with Gardenfors' developments in the 1980ies. I will present some elements of the model offered by Hans Rott (2009), as it is compatible with the older developments and offers a notation that is much easier to grasp. I use the normative models of belief revision to have a benchmark, in particular to discuss in how far these models are a better basis for discussing human inductive thought than models of Bayesian updating. Put briefly, Harman (1986) already argued that it is implausible, both psychologically and philosophically, that humans entertain a huge space of consistent conditional probabilities, but these would be required for Bayesian updating.

I will then discuss newer insights from mental model theory (Johnson-Laird) regarding if and how actual humans detect inconsistencies, and how they revise them. In the context of inductive reasoning, the unresolved and interesting question is why expert reasoners give up (or simply forget) one inductive explanation and take on another.

In total, the talk will be rather psychological. It will not have an AI or machine learning focus, but the final goal would be to understand human inductive reasoning. One aim is to work out psychological sources of differences in skill for the formation of expectations for novel opportunities that may serve as sources of advantages in forming expectations, which potentially explain puzzles in economics and management science, like the origins of competitive advantages. Three examples of sources of advantages are: The skills of decision-makers to generalize, to detect and resolve inconsistencies, and to distinguish inductive explanations that have an effect on an important consequence from others.