Agent-based modeling: a new approach for theory building in social psychology.

Most social and psychological phenomena occur not as the result of isolated decisions by individuals but rather as the result of repeated interactions between multiple individuals over time. Yet the theory-building and modeling techniques most commonly used in social psychology are less than ideal for understanding such dynamic and interactive processes. This article describes an alternative approach to theory building, agent-based modeling (ABM), which involves simulation of large numbers of autonomous agents that interact with each other and with a simulated environment and the observation of emergent patterns from their interactions. The authors believe that the ABM approach is better able than prevailing approaches in the field, variable-based modeling (VBM) techniques such as causal modeling, to capture types of complex, dynamic, interactive processes so important in the social world. The article elaborates several important contrasts between ABM and VBM and offers specific recommendations for learning more and applying the ABM approach.     

I feel our pain: Antecedents and consequences of emotional self-stereotyping

According to Intergroup Emotions Theory people categorized as group members experience the emotions of their ingroup as a consequence of that membership. Four experiments showed that participants converged toward what they believed to be their specific ingroup’s distinct emotional experience when reporting emotions as group members, but not when reporting emotions as individuals. Such self-stereotyping of ingroup emotions occurred for an experimentally fabricated ingroup as well as a range of naturally occurring groups. Demonstrating the roots of this process in categorization, self-stereotyping was increased when motivations to affiliate were amplified and was moderated by ingroup identification. The adoption of ingroup emotions changed participants’ cognitive processing in a predictable way, demonstrating that emotional self-stereotyping involved the experience rather than merely the expression of group-based emotions. Self-stereotyping of ingroup emotions is thus one mechanism by which group-based emotions are shared and can be changed.     

Subtle activation of a social categorization triggers group-level emotions

Previous research [Smith, E. R., Seger, C. R., & Mackie, D. M. (2007). Can emotions be truly group-level? Evidence regarding four conceptual criteria. Journal of Personality and Social Psychology, 93, 431-446] has demonstrated that when people are explicitly asked about the emotions they experience as members of a particular group, their reported emotions converge toward a profile typical for that group. Two studies demonstrate that the same type of convergence occurs when a group identity is made situationally salient through priming, without an explicit request to report group-level emotions. People who identify more strongly with the group converge more, and show more similarity between their group-primed emotions and explicitly reported group-level emotions. This research confirms that activating a social identity produces convergence for emotions as well as for attitudes and behaviors. It also suggests that some previous emotion research may have tapped group rather than individual-level emotions, potentially requiring some reconceptualization.     

The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity

Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as “black teenagers” are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of (1) a robot peacekeeper, (2) a self-driving car, and (3) a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology.     

Moving to Directly Cued Locations Abolishes Spatial Interference During Bimanual Actions

Interference is frequently observed during bimanual movements if the two hands perform nonsymmetric actions. We examined the source of bimanual interference in two experiments in which we compared conditions involving symmetric movements with conditions in which the movements were of different amplitudes or different directions. The target movements were cued either symbolically by letters or directly by the onset of the target locations. With symbolic cues, reaction times were longer when the movements of the two hands were not symmetric. With direct cues, reaction times were the same for symmetric and nonsymmetric movements. These results indicate that directly cued actions can be programmed in parallel for the two hands. Our results challenge the hypothesis that the cost to initiate nonsymmetric movements is due to spatial interference in a motor-programming stage. Rather, the cost appears to be caused by stimulus identification, response-selection processes connected to the processing of symbolic cues, or both.     

Understanding Ill-Structured Engineering Ethics Problems Through a Collaborative Learning and Argument Visualization Approach

As a committee of the National Academy of Engineering recognized, ethics education should foster the ability of students to analyze complex decision situations and ill-structured problems. Building on the NAE’s insights, we report about an innovative teaching approach that has two main features: first, it places the emphasis on deliberation and on self-directed, problem-based learning in small groups of students; and second, it focuses on understanding ill-structured problems. The first innovation is motivated by an abundance of scholarly research that supports the value of deliberative learning practices. The second results from a critique of the traditional case-study approach in engineering ethics. A key problem with standard cases is that they are usually described in such a fashion that renders the ethical problem as being too obvious and simplistic. The practitioner, by contrast, may face problems that are ill-structured. In the collaborative learning environment described here, groups of students use interactive and web-based argument visualization software called “AGORA-net: Participate – Deliberate!”. The function of the software is to structure communication and problem solving in small groups. Students are confronted with the task of identifying possible stakeholder positions and reconstructing their legitimacy by constructing justifications for these positions in the form of graphically represented argument maps. The argument maps are then presented in class so that these stakeholder positions and their respective justifications become visible and can be brought into a reasoned dialogue. Argument mapping provides an opportunity for students to collaborate in teams and to develop critical thinking and argumentation skills.     

Shifty characters

In “Demonstratives”, David Kaplan introduced a simple and remarkably robust semantics for indexicals. Unfortunately, Kaplan’s semantics is open to a number of apparent counterexamples, many of which involve recording devices. The classic case is the sentence “I am not here now” as recorded and played back on an answering machine. In this essay, I argue that the best way to accommodate these data is to conceive of recording technologies as introducing special, non-basic sorts of contexts, accompanied by non-basic conventions governing the use of indexicals in those contexts. The idea is that recording devices allow us to use indexicals in new and innovative ways to coordinate on objects. And, given sufficient regularity in the use of indexicals on such devices, linguistic conventions will, over time, come to reflect this innovation. I consider several alternatives to this ‘character-shifting’ theory, but none is able to account for the data as well as the present proposal. Many face additional theoretical difficulties as well. I conclude by explaining how the character-shifting theory not only retains many of the virtues of Kaplan’s original semantics, but also coheres with a plausible view on the nature of semantic theorizing more generally.