Towards a computational theory of social groups: A finite set of cognitive primitives for representing any and all social groups in the context of conflict

Pietraszewski, D. (2021). 

Behavioral and Brain Sciences, 1-62. 

doi:10.1017/S0140525X21000583

Abstract

We don't yet have adequate theories of what the human mind is representing when it represents a social group. Worse still, many people think we do. This mistaken belief is a consequence of the state of play: Until now, researchers have relied on their own intuitions to link up the concept social group on the one hand, and the results of particular studies or models on the other. While necessary, this reliance on intuition has been purchased at considerable cost. When looked at soberly, existing theories of social groups are either (i) literal, but not remotely adequate (such as models built atop economic games), or (ii) simply metaphorical (typically a subsumption or containment metaphor). Intuition is filling in the gaps of an explicit theory. This paper presents a computational theory of what, literally, a group representation is in the context of conflict: it is the assignment of agents to specific roles within a small number of triadic interaction types. This “mental definition” of a group paves the way for a computational theory of social groups—in that it provides a theory of what exactly the information-processing problem of representing and reasoning about a group is. For psychologists, this paper offers a different way to conceptualize and study groups, and suggests that a non-tautological definition of a social group is possible. For cognitive scientists, this paper provides a computational benchmark against which natural and artificial intelligences can be held.

Summary and Conclusion

Despite an enormous literature on groups and group dynamics, little attention has been paid to explicit computational theories of how the mind represents and reasons about groups. The goal of this paper has been, in a conceptual, non-technical manner, to propose a simple but non-trivial framework for starting to ask questions about the nature of the underlying representations that make the phenomenon of social groups possible—all described at the level of information processing. This computational theory, when combined with many more such theories—and followed by extensive task analyses and empirical investigations—will eventually contribute to a full accounting of the information-processing required to represent, reason about, and act in accordance with group representations.

A pdf is here.

Common Sense for A.I. Is a Great Idea

Carissa Veliz
www.slate.com
Originally posted March 19, 2018

At the moment, artificial intelligence may have perfect memories and be better at arithmetic than us, but they are clueless. It takes a few seconds of interaction with any digital assistant to realize one is not in the presence of a very bright interlocutor. Among some of the unexpected items users have found in their shopping lists after talking to (or near) Amazon’s Alexa are 150,000 bottles of shampoo, sled dogs, “hunk of poo,” and a girlfriend.

The mere exasperation of talking to a digital assistant can be enough to miss human companionship, feel nostalgia of all things analog and dumb, and foreswear any future attempts at communicating with mindless pieces of metal inexplicably labelled “smart.” (Not to mention all the privacy issues.) A.I. not understanding what a shopping list is, and the kinds of items that are appropriate to such lists, is evidence of a much broader problem: They lack common sense.

The Allen Institute for Artificial Intelligence, or AI2, created by Microsoft co-founder Paul Allen, has announced it is embarking on a new research $125 million initiative to try to change that. “To make real progress in A.I., we have to overcome the big challenges in the area of common sense,” Allen told the New York Times. AI2 takes common sense to include the “infinite set of facts, heuristics, observations … that we bring to the table when we address a problem, but the computer doesn’t.” Researchers will use a combination of crowdsourcing, machine learning, and machine vision to create a huge “repository of knowledge” that will bring about common sense. Of paramount importance among its uses is to get A.I. to “understand what’s harmful to people.”

The information is here.