Herbert Simon. Artificial intelligence as a framework for understanding intuition (original) (raw)
Related papers
Herbert Simon. Artificial intelligence
2013
Herbert Simon made overlapping substantive contributions to the fields of economics, psychology, cognitive science, artificial intelligence, decision theory, and organization theory. Si-monÕs work was motivated by the belief that neither the human mind, human thinking and decision making, nor human creativity need be mysterious. It was after he helped create ''thinking'' machines that Simon came to understand human intuition as subconscious pattern recognition. In doing so he showed that intuition need not be associated with magic and mysticism, and that it is complementary with analytical thinking. This paper will show how the overlaps in his work and especially his work on AI affected his view towards intuition.
PSL Quarterly Review, 2017
The concept of “bounded rationality” has been influential, controversial and groundbreaking, as is usually happens the case with the most important economic ideas. Over seven decades, an incredible network of new ideas, research methods and perspectives flourished, expanding the original concept to decision-making, psychology, artificial intelligence, organizational sciences, and finance. Behind the developments in these fields lies Simon’s fundamental goal: to progress improve the understanding of human thought processes. In what the following present papers, I will do not attempt an overview of the literature accumulated thus far. Instead, after a brief glance at the years during which the most important pillars of the bounded rationality concept were born, I will reconstruct the historical paths of certain relevant debates. Each of these paths cuts across many different disciplines and includes important intellectual controversies and suggestions fors future perspectives. By following these paths, we will encounter interesting conceptual affinities among between Simon, Keynes, and Schumpeter, originating in the crucial role that all of them attribute to human creativity.
Research on intuition using intuition
Handbook of Research Methods on Intuition
About a century ago Henri Bergson (1911, 1946) argued that intuition is a necessary component of philosophical inquiry, and indeed of any enterprise that seeks to understand a complex thought. To us, it therefore makes sense that, in Bergson's framework, intuition is necessary for researching intuition. Like Bergson, we do not suggest that now we should start using intuition in our researchrather, we suggest that we acknowledge that we have always been using it. Of course, this is an argument with hindsight, based on experiences from our empirical study of Nobel Laureates (NLs). In this research project, underlying the methodological argument presented here, we conducted unstructured interviews with a set of individuals who would be acknowledged as experts by the 'world at large': those awarded the highest accolade of the Nobel Prize. 1 We were not explicitly aiming at exploring the intuition of NLs, but more generally their cognitive complexity. From this inquiry, intuition has emerged as a significant characteristic of the NLs' thinking. It is of particular interest that, although we have not decided ex ante on an intuitive approach, it emerged naturally as we were trying to make sense of the interviews. Based on this inquiry, we seek to revive Bergson's interest in intuiting, and argue for the renewed importance of intuition as a method in academic research in the field of management and organizations. 1 We have been focusing on people obtaining the highest prize in their respective professions; 17 out of the 19 interviewees were Nobel Laureates but there have also been two computer scientists who have been awarded the Eckert-Mauchly prize. For simplicity, when referring to all our interviewees, we call them Nobel Laureates.
Making Better Decisions, 2020
About the Role of Intuition 2.1 Background Intuition is a very necessary element of creative work, such as research. Many famous scientists have discussed the role of intuition in their work. We provide two quotes. "Isaac Newton supposedly watched an apple fall from a tree and suddenly connected its motion as being caused by the same universal gravitational force that governs the moon's attraction to the earth." John Maynard Keynes, the famous economist, said "Newton owed his success to his muscles of intuition. Newton's powers. . ." (www.p-i-a.com/Magazine/ Issue19/Physics_19.htm). Gigerenzer, author of the book Gut Feelings: The Intelligence of the Unconscious (2008), claims that he is both intuitive and rational. "In my scientific work, I have hunches. I can't explain always why I think a certain path is the right way, but I need to trust it and go ahead. I also have the ability to check these hunches and find out what they are about. That's the science part. Now, in private life, I rely on instinct. For instance, when I first met my wife, I didn't do computations. Nor did she." (B. Kasanoff in Forbes Magazine February 21st, 2017.) But the difference between research and decision-making is that, intuition often guides research, but is subsequently subjected to rigorous laboratory and field tests. We ask that the same is done about the use of intuition in decision-making. Because solely basing your decisions (in particular, in the corporate context) on intuition, is very risky-and unnecessary. If possible, one should do some form of analysis, either to help support the intuition or challenge it. This chapter serves as motivation for us, why we often benefit from some form of analysis. Daniel Kahneman was interviewed on May 25th, 2012, for the Spiegel Online Magazine about the role of intuition in decision-making. The interview is interesting and we reproduce here the beginning of it (Also see Kahneman 2011).
Herbert Simon's Decision-Making Approach: Investigation of Cognitive Processes in Experts
Review of General Psychology, 2010
Herbert Simon's research endeavor aimed to understand the processes that participate in human decision making. However, despite his effort to investigate this question, his work did not have the impact in the “decision making” community that it had in other fields. His rejection of the assumption of perfect rationality, made in mainstream economics, led him to develop the concept of bounded rationality. Simon's approach also emphasized the limitations of the cognitive system, the change of processes due to expertise, and the direct empirical study of cognitive processes involved in decision making. In this article, we argue that his subsequent research program in problem solving and expertise offered critical tools for studying decision-making processes that took into account his original notion of bounded rationality. Unfortunately, these tools were ignored by the main research paradigms in decision making, such as Tversky and Kahneman's biased rationality approach (als...
The Concept of Intuition in Artificial Intelligence
DISCUSSION: current developments in the implementation of human intuitive processes in artificial intelligence (AI) based on the review of literature. The concept of intuition has been discussed in various fields of cognitive science – psychology, philosophy, economics, and artificial intelligence, however, it remains an underresearched area. The existing theories and models offer some worthy explanations of the phenomenon but lack methodology for its practical implementation. The research on intuition based methods in artificial intelligence and machine learning has encountered both conceptual and practical difficulties. In particular, implementation problems are due to the fact that mental intuitive processes do not yield to straightforward explanations in terms of mathematical representation. Moreover, the concept of intuition as such has not been studied sufficiently in other fields of cognitive science.
Intuition's Role in Making Decisions
Competitive Intelligence Magazine, 2007
Typically, managers assume better decisions are a matter of combining better inputs with better analysis, leading to better prediction, planning and execution. These assumptions are based on persistent, fundamental misunderstandings about the inputs to decisions, the outcomes from decisions, and the very nature of "deciding."