scholarly journals Large prospective losses lead to sub-optimal sensorimotor decisions in humans

2018 ◽  
Author(s):  
Tyler J. Adkins ◽  
Richard L. Lewis ◽  
Taraz G. Lee
Keyword(s):  
Bounded Rationality ◽  
Recent Work ◽  
Human Behavior ◽  
Strong Evidence ◽  
Decision Problem ◽  
Computational Models ◽  
Cognitive Biases ◽  
Expected Gain ◽  
Potential Losses ◽  
Biases And Heuristics

AbstractThe rationality of human behavior has been a major problem in philosophy for centuries. The pioneering work of Kahneman and Tversky provides strong evidence that people are not rational. Recent work in psychophysics argues that incentivized sensorimotor decisions (such as deciding where to reach to get a reward) maximizes expected gain, suggesting that it may be impervious to cognitive biases and heuristics. We rigorously tested this hypothesis using multiple experiments and multiple computational models. We obtained strong evidence that people deviated from the objectively rational strategy when potential losses were large. They instead appeared to follow a strategy in which they simplify the decision problem and satisfice rather than optimize. This work is consistent with the framework known as bounded rationality, according to which people behave rationally given their computational limitations.

Cognitive biases and limitations

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Human Behavior ◽  
Cognitive Biases ◽  
Basic Assumptions ◽  
Human Thinking

This chapter summarizes some of the most common cognitive biases and limitations in human thinking and provides specific strategies for what we can do about them in various contexts. It serves as a baseline for understanding the flaws in some of our basic assumptions about human behavior and for approaching the rest of the theories discussed in the book with an appropriate dose of humility.

scholarly journals A computational reward learning account of social media engagement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Björn Lindström ◽  
Martin Bellander ◽  
David T. Schultner ◽  
Allen Chang ◽  
Philippe N. Tobler ◽  
...  
Keyword(s):  
Social Media ◽  
Human Behavior ◽  
Computational Models ◽  
Average Rate ◽  
Human Life ◽  
Modern Human ◽  
Reward Learning ◽  
Learning Mechanisms ◽  
Social Media Engagement ◽  
Social Rewards

AbstractSocial media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

scholarly journals Computational Modeling of Music Cognition: A Case Study on Model Selection

2006 ◽  
Vol 23 (5) ◽  
pp. 365-376 ◽  
Author(s):  
Henkjan Honing
Keyword(s):  
Model Selection ◽  
Strong Evidence ◽  
Goodness Of Fit ◽  
Computational Models ◽  
Recent Literature ◽  
Music Cognition ◽  
Kinematic Model ◽  
Music Performance ◽  
Limited Range

While the most common way of evaluating a computational model is to see whether it shows a good fit with the empirical data, recent literature on theory testing and model selection criticizes the assumption that this is actually strong evidence for the validity of a model. This article presents a case study from music cognition (modeling the ritardandi in music performance) and compares two families of computational models (kinematic and perceptual) using three different model selection criteria: goodness-of-fit, model simplicity, and the degree of surprise in the predictions. In the light of what counts as strong evidence for a model’s validity—namely that it makes limited range, nonsmooth, and relatively surprising predictions—the perception-based model is preferred over the kinematic model.

Economics and Epicycles

2020 ◽  
pp. 174569162095378
Author(s):  
Satoshi Kanazawa
Keyword(s):  
Behavioral Economics ◽  
Human Behavior ◽  
Evolutionary Biology ◽  
Cognitive Biases ◽  
Behavioral Economic ◽  
Nonhuman Animals ◽  
Human Actors ◽  
Economic Studies

I aver that standard economics as a model of human behavior is as incorrect in 2017 (after Thaler) as geocentrism was as a model of celestial behavior in 1617 (after Galileo). Behavioral economic studies that have exposed the paradoxes and anomalies in standard economics are akin to epicycles on geocentrism. Just as no amount of epicycles could salvage geocentrism as a model of celestial behavior because it was fundamentally incorrect, no amount of behavioral economic adjustments could salvage standard economics as a model of human behavior because it is fundamentally incorrect. Many of the cognitive biases exhibited by humans are shared by other species, so not only are human actors Humans (as opposed to Econs), but nonhuman animals as phylogenetically distant from humans as ants and locusts are also Humans. Evolutionary biology as a model of human behavior can explain many of the hitherto unexplained cognitive biases and provide a unifying model of human behavior currently lacking in behavioral economics.

scholarly journals Why bright city lights dazzle and illuminate: A cognitive science approach to urban promises

Urban Studies ◽  
2018 ◽  
Vol 56 (2) ◽  
pp. 452-470 ◽  
Author(s):  
Rodrigo Cardoso ◽  
Evert Meijers ◽  
Maarten van Ham ◽  
Martijn Burger ◽  
Duco de Vos
Keyword(s):  
Imperfect Information ◽  
Cognitive Biases ◽  
Urban Life ◽  
Urban Migration ◽  
Economic Prosperity ◽  
Urban Migrants ◽  
Urban Futures ◽  
The Many ◽  
Biases And Heuristics ◽  
Human Rationality

Despite the many uncertainties of life in cities, promises of economic prosperity, social mobility and happiness have fuelled the imagination of generations of urban migrants in search of a better life. Access to jobs, housing and amenities, and fewer restrictions of personal choices are some of the perceived advantages of cities, characterised here as ‘urban promises’. But while discourses celebrating the triumph of cities became increasingly common, urban rewards are not available everywhere and for everyone. Alongside opportunity, cities offer inequality, conflict and poor living conditions. Their narrative of promise has been persistent across different times and places, but the outcomes and experiences of urban life compare poorly with the overoptimistic expectations of many newcomers. And yet, millions still come and stay regardless of odds, raising the question why we have such positive and persistent expectations about cities. To examine this question, this paper considers the process of urban migration from the perspective of decision-making under uncertainty. It discusses how decisions and evaluations are based on imperfect information and offers a novel contribution by examining how the cognitive biases and heuristics which restrict human rationality shape our responses to urban promises. This approach may allow a better understanding of how people make decisions regarding urban migration, how they perceive their urban experiences and evaluate their life stories. We consider the prospects and limitations of the behavioural approach and discuss how biases favouring narratives of bright urban futures can be exploited by ‘triumphalist’ accounts of cities which neglect their embedded injustices.

scholarly journals The Repeated Recording Illusion

2017 ◽  
Vol 35 (1) ◽  
pp. 94-117 ◽  
Author(s):  
Manuel Anglada-Tort ◽  
Daniel Müllensiefen
Keyword(s):  
Individual Difference ◽  
Cognitive Biases ◽  
Repeated Exposure ◽  
Systematic Measurement ◽  
Experimental Paradigm ◽  
Musical Performances ◽  
Explicit Information ◽  
Biases And Heuristics ◽  
High Prestige

The repeated recording illusion refers to the phenomenon in which listeners believe to hear different musical stimuli while they are in fact identical. The present paper aims to construct an experimental paradigm to enable the systematic measurement of this phenomenon, investigating potentially related extrinsic and individual difference factors. Participants were told to listen to “different” musical performances of an original piece when in fact they were exposed to the same repeated recording. Each time, the recording was accompanied by a text suggesting a low, medium, or high prestige of the performer. Most participants (75%) believed that they had heard different musical performances. Participants with high levels of neuroticism and openness were significantly more likely to fall for the illusion. While the explicit information presented with the music influenced participants’ ratings significantly, the effect of repeated exposure was only significant in the more familiar music condition. These results suggest that like many other human judgments, evaluations of music also rely on cognitive biases and heuristics that do not depend on the stimuli themselves. The repeated recording illusion can constitute a useful paradigm for investigating nonmusical factors because it allows for the study of their effects while the music remains the same.

Networks of Photos, Landmarks, and People

Leonardo ◽  
2011 ◽  
Vol 44 (3) ◽  
pp. 240-243 ◽  
Author(s):  
David Crandall ◽  
Noah Snavely
Keyword(s):  
Recent Work ◽  
Human Behavior ◽  
Visual Representations ◽  
3D Models ◽  
Global Scale ◽  
Local Scale ◽  
Photo Sharing ◽  
The World ◽  
Vantage Points ◽  
Combining Information

Social photo-sharing sites like Flickr contain vast amounts of latent information about the world and human behavior. The authors describe their recent work in building automatic algorithms that analyze large collections of imagery in order to extract some of this information. At a global scale, geo-tagged photographs can be used to identify the most photographed places on Earth, as well as to infer the names and visual representations of these places. At a local scale, the authors build detailed 3D models of a scene by combining information from thousands of 2D photographs taken by different people and from different vantage points.

scholarly journals 'Do You see what I'm Dealing with here?' Vicious circles in conflict

2014 ◽  
Vol 1 (1) ◽  
pp. 30-38
Keyword(s):  
Cognitive Biases ◽  
The Self ◽  
Fundamental Attribution Error ◽  
Attribution Error ◽  
Biases And Heuristics ◽  
Bullying And Harassment

We know that our thinking is affected by conflict; this applies to groups and nations as much as to individuals. Mediators are at the sharp end of this phenomenon, and those we work with often find each other’s behaviour at best inexplicable and at worst malicious. This article considers how biases and heuristics (mental shortcuts) can exacerbate disputes. Two cognitive biases in particular can contribute to the growth of conflict: the fundamental attribution error and the self-serving bias. Using a workplace mediation case study the article traces the step-by-step mechanics of conflict in people’s thinking and its tendency to set in motion vicious circles of suspicion and defence. It goes on to provide a critique of bullying and harassment policies before proposing that they begin with a mediation stage in order to combat attribution errors by bringing more data into play.

Agent-based mechanism design – investigating bounded rationality concepts in a budgeting context

2017 ◽  
Vol 23 (1/2) ◽  
pp. 13-27 ◽  
Author(s):  
Iris Lorscheid ◽  
Matthias Meyer
Keyword(s):  
Mechanism Design ◽  
Bounded Rationality ◽  
Human Behavior ◽  
Design Methodology ◽  
Content Type ◽  
Agent Based ◽  
Decision Context ◽  
Agent Learning ◽  
Groves Mechanism ◽  
Research Questions

Purpose This study aims to demonstrate how agent-based simulation (ABS) may provide a computational testbed for mechanism design using concepts of bounded rationality (BR). ABS can be used to systematically derive and formalize different models of BR. This allows us to identify the cognitive preconditions for behavior intended by the mechanism and thereby to derive implications for the design of mechanisms. Design/methodology/approach Based on an analysis of the requirements of the decision context, the authors describe a systematic way of incorporating different BR concepts into an agent learning model. The approach is illustrated by analyzing an incentive scheme suggested for truthful reporting in budgeting contexts, which is an adapted Groves mechanism scheme. Findings The study describes systematic ways in which to derive BR agents for research questions where behavioral aspects might matter. The authors show that BR concepts may lead to other outcomes than the intended truth-inducing effect. A modification of the mechanism to more distinguishable levels of payments improves the results in terms of the intended effect. Research limitations/implications The presented BR concepts as simulated by agent models cannot model human behavior in its full complexity. The simplification of complex human behavior is a useful analytical construct for the controlled analysis of a few aspects and an understanding of the potential consequences of those aspects of human behavior for mechanism design. Originality/value The paper specifies the idea of a computational testbed for mechanism design based on BR concepts. Beyond this, a systematic and stepwise approach is shown to formalize bounded rational behavior by agents based on a requirements analysis, including benchmark models for the comparison and evaluation of BR concepts.

Forgiving, forgetting and reframing

2020 ◽  
pp. 146-161
Author(s):  
John Lippitt
Keyword(s):  
Social Psychology ◽  
Recent Work ◽  
Cognitive Biases ◽  
Per Se

This chapter addresses two issues. First, forgiving is often connected with—but distinguished from—forgetting. The chapter returns to Kierkegaard’s typically overlooked distinction between forgetting per se and ‘forgetting in forgiveness’. Like Kierkegaard, Jeffrey Blustein is unusual in taking the idea of forgetting in forgiveness seriously. However, through a dialogue with Blustein, it is argued that ‘forgetting’ is not the best way of capturing the position for which both he and Kierkegaard are trying to make space. The discussion also draws on Blustein to start to explore what might make the disposition of ‘forgivingness’ a virtue. The second issue is the important question of cognitive biases. Through a discussion of some recent work in social psychology and behavioural ethics, the role such biases might play in blocking our ability to forgive is considered, as is how important transcending them might be to the process of ‘reframing’ the wrongdoer.

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