Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems

Media, racism and public health psychology

A growing literature has established that racism contributes to ill-health of migrants, minority group members and indigenous peoples. Racial discrimination has been shown to act at personal, institutional and societal levels, negatively affecting physical health as evidenced by heart disease and other stress related conditions and generally negating wellbeing, signalled by psychological and psychiatric disorders including depression. In our highly mediatized world, mass communications in diverse forms are decisive for people’s knowledge and understandings of the world and their place in it. From critical studies we know that the media consistently marginalize, denigrate and neglect particular ethnic and cultural groups. Where media do focus on such groups much of the reporting is negative and stereotyping. Achievements are ignored or minimized while representations of those groups as problems for and threats to the dominant are highlighted. In this paper we consider the particular case of media representations of the indigenous Maori of Aotearoa New Zealand. We review extant studies to argue that detailed and systematic study is necessary for the development of critical, local media scholarship. Such scholarship is necessary if the current media impact on Maori health and wellbeing is to be mitigated. While such considerations may not have been traditional concerns of health psychology we, following George Albee (2003), argue for them as affirming the need for critical public health psychology

How much of commonsense and legal reasoning is formalizable? A review of conceptual obstacles

Fifty years of effort in artificial intelligence (AI) and the formalization of legal reasoning have produced both successes and failures. Considerable success in organizing and displaying evidence and its interrelationships has been accompanied by failure to achieve the original ambition of AI as applied to law: fully automated legal decision-making. The obstacles to formalizing legal reasoning have proved to be the same ones that make the formalization of commonsense reasoning so difficult, and are most evident where legal reasoning has to meld with the vast web of ordinary human knowledge of the world. Underlying many of the problems is the mismatch between the discreteness of symbol manipulation and the continuous nature of imprecise natural language, of degrees of similarity and analogy, and of probabilities

ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., "if X pays Y a compliment, then Y will likely return the compliment"). We propose nine if-then relation types to distinguish causes vs. effects, agents vs. themes, voluntary vs. involuntary events, and actions vs. mental states. By generatively training on the rich inferential knowledge described in ATOMIC, we show that neural models can acquire simple commonsense capabilities and reason about previously unseen events. Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.Comment: AAAI 2019 C

Advancing Computational Models of Narrative

Report of a Workshop held at the Wylie Center, Beverly, MA, Oct 8-10 2009Sponsored by the AFOSR under MIT-MURI contract #FA9550-05-1-032