KittyCat: a cognitive model of structure-form discovery

Cognition is a core subject to understand how humans think and behave. In that sense, it is clear that Cognition is a great ally to Management, as the later deals with people and is very interested in how they behave, think, and make decisions. However, even though Cognition shows great promise as a field, there are still many topics to be explored and learned in this fairly new area. Kemp & Tenembaum (2008) tried to a model graph-structure problem in which, given a dataset, the best underlying structure and form would emerge from said dataset by using bayesian probabilistic inferences. This work is very interesting because it addresses a key cognition problem: learning. According to the authors, analogous insights and discoveries, understanding the relationships of elements and how they are organized, play a very important part in cognitive development. That is, this are very basic phenomena that allow learning. Human beings minds do not function as computer that uses bayesian probabilistic inferences. People seem to think differently. Thus, we present a cognitively inspired method, KittyCat, based on FARG computer models (like Copycat and Numbo), to solve the proposed problem of discovery the underlying structural-form of a dataset.

A model of the rule of law

Economic development requires some limits on what those in power can do | the rule of law | but how can restraints be imposed on the powerful when there is no-one above them? This paper studies equilibrium rules allocating power and resources established by selfinterested incumbents under the threat of rebellions from inside and outside the group in power. Commitment to uphold individuals' rights can only be achieved if power is not as concentrated as incumbents would like it to be, ex post. Power sharing endogenously enables incumbents to commit to otherwise time-inconsistent laws by ensuring more people receive rents under the status quo, and thus want to defend it.