Representing qualitative social science in computational models to aid reasoning under uncertainty: National security examples

2016 ◽  
Vol 14 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Paul K Davis ◽  
Angela O’Mahony
Keyword(s):  
Social Science ◽  
Computational Modeling ◽  
National Security ◽  
Computational Models ◽  
Empirical Work ◽  
Empirical Testing ◽  
Special Issue ◽  
Reasoning Under Uncertainty ◽  
Social Science Knowledge ◽  
Qualitative Models

Representing causal social science knowledge in models is difficult: much of the best knowledge is qualitative and ambiguously conditional, unlike the knowledge in “physics models.” This paper describes a stream of RAND research that began with qualitative models providing a structured depiction of casual factors creating effects. That has subsequently been extended to an unusual kind of uncertainty sensitive computational modeling that enables exploratory reasoning and analysis. We illustrate the approach with applications to counterterrorism, detection of terrorists, and nuclear crises. We believe that the approach will complement other approaches that can reflect social science phenomena [see other papers in this special issue of JDMS] and that the approach has broad potential within and beyond the national security domain. We also believe that it has the potential to inform empirical work—encouraging a transition from the step-by-step empirical testing of simple discrete hypotheses to the testing and refinement of more comprehensive causal models.

scholarly journals Computational modeling of bilingualism: How can models tell us more about the bilingual mind?

2013 ◽  
Vol 16 (2) ◽  
pp. 241-245 ◽  
Author(s):  
PING LI
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
Central Question ◽  
Special Issue

Models are no new beasts to scholars of bilingualism. During the last several decades we have seen many interesting and important models that postulate how the bilingual mind works. But specific, computationally implemented, models are far less common than general, verbal, models of bilingualism. This is because the former require efforts on the part of the researcher to conduct algorithmic and representational implementations, whereas the latter do not. The central question is: What good does implementation do in telling us about the bilingual mind beyond what the verbal models do? This Special Issue is an attempt to address this question with seven computational models of bilingualism from different research labs.

scholarly journals Computational Social Creativity

2015 ◽  
Vol 21 (3) ◽  
pp. 366-378 ◽  
Author(s):  
Rob Saunders ◽  
Oliver Bown
Keyword(s):  
Social Science ◽  
Computational Modeling ◽  
Artificial Life ◽  
Computational Models ◽  
Critical Role ◽  
Computational Social Science ◽  
Social Phenomena ◽  
Creativity Research ◽  
Social Creativity ◽  
Carry Over

This article reviews the development of computational models of creativity where social interactions are central. We refer to this area as computational social creativity. Its context is described, including the broader study of creativity, the computational modeling of other social phenomena, and computational models of individual creativity. Computational modeling has been applied to a number of areas of social creativity and has the potential to contribute to our understanding of creativity. A number of requirements for computational models of social creativity are common in artificial life and computational social science simulations. Three key themes are identified: (1) computational social creativity research has a critical role to play in understanding creativity as a social phenomenon and advancing computational creativity by making clear epistemological contributions in ways that would be challenging for other approaches; (2) the methodologies developed in artificial life and computational social science carry over directly to computational social creativity; and (3) the combination of computational social creativity with individual models of creativity presents significant opportunities and poses interesting challenges for the development of integrated models of creativity that have yet to be realized.

Translating Informal Theories Into Formal Theories: The Case of the Dynamic Computational Model of the Integrated Model of Work Motivation

2018 ◽  
Vol 23 (2) ◽  
pp. 238-274 ◽  
Author(s):  
Jeffrey B. Vancouver ◽  
Mo Wang ◽  
Xiaofei Li
Keyword(s):  
Computational Modeling ◽  
Computational Model ◽  
Empirical Data ◽  
Computational Models ◽  
Work Motivation ◽  
Empirical Work ◽  
Formal Theory ◽  
Integrated Model ◽  
Static Model ◽  
The Core

Theories are the core of any science, but many imprecisely stated theories in organizational and management science are hampering progress in the field. Computational modeling of existing theories can help address the issue. Computational models are a type of formal theory that are represented mathematically or by other formal logic and can be simulated, allowing theorists to assess whether the theory can explain the phenomena intended as well as make testable predictions. As an example of the process, Locke’s integrated model of work motivation is translated into static and dynamic computational models. Simulations of these models are compared to the empirical data used to develop and test the theory. For the static model, the simulations revealed largely strong associations with robust empirical findings. However, adding dynamics created several challenges to key precepts of the theory. Moreover, the effort revealed where empirical work is needed to further refine or refute the theory. Discussion focuses on the value of computational modeling as a method for formally testing, pruning, and extending extant theories in the field.

Introduction: The Anthropocene as Context and Concept for the Study of Higher Education

2019 ◽  
Vol 1 (1) ◽  
pp. 1-15
Author(s):  
RYAN EVELY GILDERSLEEVE ◽  
KATIE KLEINHESSELINK
Keyword(s):  
Higher Education ◽  
Social Science ◽  
Social Institutions ◽  
Special Issue ◽  
Analytic Tool ◽  
The Social ◽  
Geologic Condition

The Anthropocene has emerged in philosophy and social science as a geologic condition with radical consequence for humankind, and thus, for the social institutions that support it, such as higher education. This essay introduces the special issue by outlining some of the possibilities made available for social/philosophical research about higher education when the Anthropocene is taken seriously as an analytic tool. We provide a patchwork of discussions that attempt to sketch out different ways to consider the Anthropocene as both context and concept for the study of higher education. We conclude the essay with brief introductory remarks about the articles collected for this special issue dedicated to “The Anthropocene and Higher Education.”

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals From Abbott Thayer to the present day: what have we learned about the function of countershading?

2008 ◽  
Vol 364 (1516) ◽  
pp. 519-527 ◽  
Author(s):  
Hannah M Rowland
Keyword(s):  
Empirical Work ◽  
Quantitative Measure ◽  
Convincing Evidence ◽  
Colour Pattern ◽  
Protective Effects ◽  
Empirical Testing ◽  
Background Matching ◽  
Visual Characteristics ◽  
Subjective Evidence ◽  
The Many

Of the many visual characteristics of animals, countershading (darker pigmentation on those surfaces exposed to the most lighting) is one of the most common, and paradoxically one of the least well understood. Countershading has been hypothesized to reduce the detectability of prey to visually hunting predators, and while the function of a countershaded colour pattern was proposed over 100 years ago, the field has progressed slowly; convincing evidence for the protective effects of countershading has only recently emerged. Several mechanisms have been invoked for the concealing function of countershading and are discussed in this review, but the actual mechanisms by which countershading functions to reduce attacks by predators lack firm empirical testing. While there is some subjective evidence that countershaded animals match the background on which they rest, no quantitative measure of background matching has been published for countershaded animals; I now present the first such results. Most studies also fail to consider plausible alternative explanations for the colour pattern, such as protection from UV or abrasion, and thermoregulation. This paper examines the evidence to support each of these possible explanations for countershading and discusses the need for future empirical work.

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