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Decision-making takes us from predictions to actions
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Dr Howard Rankin write about "The Psychology of Decision-Making"
Michael Hentschel explains "Predictions and Decisions"
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TRADING PERFORMANCE RESULTS OF THE INTUALITYAI SYSTEM HAVE MANY INHERENT LIMITATIONS. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN. IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN REPORTED PERFORMANCE RESULTS AND RESULTS SUBSEQUENTLY ACHIEVED BY THE SYSTEM OR PORTFOLIO. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF THE SYSTEM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF REPORTED PERFORMANCE RESULTS AND ALL OF WHICH CAN ADVERSELY AFFECT ACTUAL TRADING RESULTS.
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The Psychology of Decision-Making
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Decision-making is a ubiquitous cognitive process that shapes every facet of human existence, from everyday choices to monumental life-altering decisions. Psychologists have delved into the complex terrain of decision-making for decades, unraveling the multifaceted nature of this process and shedding light on the cognitive, emotional, and social factors that influence our choices.
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This piece explores the psychology of decision-making, highlighting key theories and studies that contribute to our understanding of this intricate phenomenon.
Dual-Process Theory: One prominent framework in the psychology of decision-making is the dual-process theory. This theory posits that decision-making involves two distinct cognitive systems: the intuitive, automatic system (System 1) and the deliberative, analytical system (System 2). System 1 relies on heuristics and shortcuts, allowing for quick decisions based on past experiences and emotions. In contrast, System 2 involves conscious reasoning and is resource-intensive. Kahneman and Tversky's groundbreaking work on prospect theory and biases exemplifies this dual-process approach, revealing how cognitive biases can lead to deviations from rational decision-making.
Behavioral Economics: integrates psychological insights into economic models, challenging the traditional view of humans as purely rational decision-makers. This approach emphasizes the role of emotions, social influences, and cognitive biases in shaping choices. Concepts like loss aversion, framing effects, and the endowment effect showcase how psychological factors lead individuals to make suboptimal decisions that deviate from normative economic models.
Decision-Making under Uncertainty: The psychology of decision-making is particularly relevant when facing uncertainty. Prospect theory, for instance, highlights how individuals evaluate potential gains and losses relative to a reference point, rather than in absolute terms. This tendency contributes to the framing effect, where choices are influenced by how options are presented. Moreover, studies on ambiguity aversion reveal that people often prefer known risks over uncertain outcomes.
Emotional Influences on Decision-Making: play a crucial role in decision-making, influencing both cognitive processes and preferences. The somatic marker hypothesis suggests that bodily sensations and emotional responses guide decision-making, particularly in complex and uncertain situations. Emotional decision-making is exemplified by the Iowa Gambling Task, where participants with damage to emotional brain areas make risky choices due to impaired emotional feedback.
Social Context: Decision-making is not isolated but is heavily influenced by social context. Social comparison theory posits that people evaluate their options based on others' choices, leading to conformity and peer pressure. Additionally, the concept of groupthink highlights the dangers of group decision-making, where a desire for consensus can suppress dissenting opinions, leading to suboptimal choices.
The psychology of decision-making is a rich field of study that encompasses cognitive, emotional, and social dimensions. The interplay between automatic and analytical thinking, emotional influences, cognitive biases, and social factors collectively shape the choices individuals make. By delving into these intricate mechanisms, psychologists continue to unveil the complexities of decision-making, contributing to a deeper understanding of human behavior and providing insights into how to make better, more informed choices.
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References
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
Ariely, D. (2008). Predictably irrational: The hidden forces that shape our decisions. HarperCollins.
Camerer, C. F., & Weber, M. (1992). Recent developments in modeling preferences: Uncertainty and ambiguity. Journal of Risk and Uncertainty, 5(4), 325-370.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323.
Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 351(1346), 1413-1420.
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1-3), 7-15.
Social Influences on Decision-Making:
Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2), 117-140.
Janis, I. L. (1972). Victims of groupthink: A psychological study of foreign-policy decisions and fiascoes. Houghton Mifflin.
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by Howard Rankin PhD, Science Director, psychology and cognitive neuroscience
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Predictions and Decisions
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Humans have distinct advantages over artificial intelligence when it comes to making probabilistic decisions and predictions. While AI can process huge amounts of data and calculate probabilities quickly, humans draw on experience, intuition, and complex reasoning that allows us to make judgments that go beyond pure statistics.
One advantage of human prediction is our ability to mentally simulate hypothetical scenarios.
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Studies of mental imagery show humans can imaginatively run "what-if" simulations in our minds, working through multiple future scenarios to prepare for various contingencies in a way beyond current AI. Human social cognition also gives us advantages in probabilistic prediction.
Theory of mind, the capacity to attribute mental states to others, allows us to anticipate others' beliefs, desires, and probable actions. Our ability to tell stories and craft narratives also supports probabilistic forecasting by helping us weave together chains of probable events.
Overall, research shows humans have complementary strengths that enhance probabilistic decision-making and prediction. While AI provides an essential role in processing huge amounts of data, human abilities like emotion, mental simulation, and social cognition allow us to go beyond the numbers and statistics. Combining human strengths with AI offers the best probabilistic judgments and forecasts. Understanding the advantages of human cognition can help guide developing AI that works symbiotically with human strengths.
Our ability to understand and model other people's motivations and goals enhances human predictive abilities in a few key ways:
It allows us to anticipate a wider range of probable actions. When we know someone's motivations, we can simulate and predict the variety of potential behaviors that would fulfill those motivations. If we know someone is motivated by ambition, we can predict they may work late, take on extra projects, ask for promotions, etc.
It helps us assign different probabilities to potential outcomes based on goal relevance. If we know someone's main goal is saving money, we can predict they are more likely to choose cheaper options over more expensive ones across different decisions.
It allows us to make predictions that go beyond surface behaviors. When we understand motivations, we can better predict not just what people will do, but why. This helps us explain and anticipate even unusual decisions if we realize how they connect to underlying goals.
It helps us predict changes in behavior over time as goals shift. Goals and motivations are not static. As circumstances change, different goals may become more salient. Tracking this helps us better adapt our predictions.
It anchors predictions in stable psychological traits rather than situational factors alone. Goals arise from personality and values that persist in ways discrete behaviors may not.
It provides insight into the subjective benefit driving behaviors. When we appreciate the emotional, social, intellectual, or other gain behind others' goals, we can better calculate the perceived value attached to different actions and outcomes.
Overall, by building models of the goals and motivations that drive people's decision-making, we can generate richer and more accurate theories of behavior to simulate a wider possibility space and make better probabilistic predictions. Our social cognition abilities allow us to move beyond superficial predictions by understanding the deeper why behind others' choices
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References:
Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430-454.
Adolphs, R. (2009). The social brain: neural basis of social knowledge. Annual review of psychology, 60, 693-716.
Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80(4), 237-251. https://doi.org/10.1037/h0034747
Gilbert, D.T., & Wilson, T.D. (2007). Prospection: Experiencing the future. Science, 317(5843), 1351-1354. https://doi.org/10.1126/science.1144161
Dunbar, R.I. (1998). The social brain hypothesis. Evolutionary Anthropology: Issues, News, and Reviews, 6(5), 178-190. https://doi.org/10.1002/(SICI)1520-6505(1998)6:5 ↗%3C178::AID-EVAN5%3E3.0.CO;2-8
Adolphs, R. (2010). Conceptual challenges and directions for social neuroscience. Neuron, 65(6), 752-767. https://doi.org/10.1016/j.neuron.2010.03.006
Olsson, A., & Ochsner, K.N. (2008). The role of social cognition in emotion. Trends in Cognitive Sciences, 12(2), 65-71. https://doi.org/10.1016/j.tics.2007.11.010
Krueger, J.I., & Funder, D.C. (2004). Towards a balanced social psychology: Causes, consequences and cures for the problem-seeking approach to social behavior and cognition. Behavioral and Brain Sciences, 27(3), 313-327. https://doi.org/10.1017/S0140525X04000081
Malle, B.F. (2004). How the mind explains behavior: Folk explanations, meaning, and social interaction. MIT Press. https://direct.mit.edu/books/book/2323/How-the-Mind-Explains-Behavior
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by Michael Hentschel, anthropologist, economist, venture capitalist
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