Global-Local Incompatibility: The Misperception of Reliability in Judgment Regarding Global Variables

Quintessential Solutions to Existential Problems: How Human Factors and Ergonomics Can and Should Address the Imminent Challenges of Our Times

OBJECTIVE

To examine and evaluate ways in which an understanding of the quintessential element of Human Factors/Ergonomics can address the spectrum of existential threats that confront contemporary civilization.

BACKGROUND

HF/E is dedicated to improving quality of life. Paradoxically, many processes which sustain contemporary civilization act to reduce that overall quality. Some technological developments themselves now even present existential threats to the fragile skein of civilization itself. Many disciplines address these diverse threats, and each may be advised and facilitated by HF/E knowledge and methods. It is a moral imperative of our science to contribute what we can to proposed resolutions.

METHOD

A primary conduit, by the established strengths of HF/E can contribute to potential solutions is identified. The present work advocates for specific, practical interventions using a direct-perception mediated, panopticon principle, that derives from the corpus of our science.

RESULT

Limitations upon a general, social understanding of imminent global concerns, which are largely ignorable when not actually present, are brought to immediate consciousness via an HF/E principle emphasizing the direct-perception of threat. It is argued that this, and allied HF/E insights can generate practical steps toward problem resolution at both macroscopic and localized levels of implementation.

APPLICATIONS

The primary, practical application of the proposed panopticon principle is to use our science to save global civilization. It is postulated that this represents useful employment of the knowledge we have adduced and accumulated across our discipline's existence.

The Strengths and Weaknesses of Crowds to Address Global Problems

Global climate change, the COVID-19 pandemic, and the spread of misinformation on social media are just a handful of highly consequential problems affecting society. We argue that the rough contours of many societal problems can be framed within a "wisdom of crowds" perspective. Such a framing allows researchers to recast complex problems within a simple conceptual framework and leverage known results on crowd wisdom. To this end, we present a simple "toy" model of the strengths and weaknesses of crowd wisdom that easily maps to many societal problems. Our model treats the judgments of individuals as random draws from a distribution intended to represent a heterogeneous population. We use a weighted mean of these individuals to represent the crowd's collective judgment. Using this setup, we show that subgroups have the potential to produce substantively different judgments and we investigate their effect on a crowd's ability to generate accurate judgments about societal problems. We argue that future work on societal problems can benefit from more sophisticated, domain-specific theory and models based on the wisdom of crowds.

One Fee, Two Fees; Red Fee, Blue Fee: People Use the Valence of Others’ Speech in Social Relational Judgments

We present an empirical demonstration that people rely on linguistic valence as a direct cue to a speaker’s group membership. Members of the U.S. voting public judge positive words as more likely to be spoken by members of their political in-group, and negative words as more likely to be spoken by members of their political out-group (three studies with 655 participants). We further find that participants perceive pluralized forms of nouns as more extremely valenced than singular forms (one study with 280 participants). This allowed us to control for the semantic content of words while eliciting systematic differences in the source attributions made by partisans. Our work contributes to both theory and methodology used to understand the linguistic cues people use to make social relational judgments.

A Social Interpolation Model of Group Problem-Solving

How do people use information from others to solve complex problems? Prior work has addressed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (SIM), an agent-based model to explore the cognitive mechanisms that can underlie exploratory behavior in groups. Through results from simulation experiments, we conclude that "exploration" may not be a single cognitive property, but rather the emergent result of three distinct behavioral and cognitive mechanisms, namely, (a) breadth of generalization, (b) quality of prior expectation, and (c) relative valuation of self-obtained information. We formalize these mechanisms in the SIM, and explore their effects on group dynamics and success at solving different kinds of problems. Our main finding is that broad generalization and high quality of prior expectation facilitate successful search in environments where exploration is important, and hinder successful search in environments where exploitation alone is sufficient.