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Paul E. Smaldino  - - - 
Top co-authors See all
Marco A. Janssen

153 shared publications

School of SustainabilityArizona State University Tempe AZ USA

Damien R. Farine

99 shared publications

Edward Grey Institute of Field Ornithology, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK

Peter J. Richerson

70 shared publications

Department of Environmental Science and Policy, University of California, Davis, CA, USA

Michael D. Makowsky

37 shared publications

Center for Advanced Modeling in the Social, Behavioral, and Health Sciences, Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD, USA

Jeffrey C Schank

28 shared publications

Department of Psychology, University of California, Davis, Davis, CA, United States

Publication Record
Distribution of Articles published per year 
(1999 - 2019)
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Publications See all
PREPRINT-CONTENT 0 Reads 0 Citations Five models of science, illustrating how selection shapes methods Paul Smaldino Published: 11 April 2019
doi: 10.31235/
DOI See at publisher website ABS Show/hide abstract
Science involves both theory building and fact finding. This chapter focuses on the fact- finding aspect. In this sense, science can be viewed as a process of signal detection for facts. We wish to discover true associations between variables. However, our methods for measurement are imprecise. We sometimes mistake noise for signal, and vice versa. How we conceptualize the scientific enterprise shapes how we go about the business of conducting research as well as how we strive to improve scientific practices. In this chapter, I’ll present several models of science. I’ll begin by showing ways in which the classic “hypothesis testing” model of science is misleading, and leads to flawed inferences. As a remedy, I’ll discuss models that treat science as a population process, with important dynamics at the group level that trickle down to the individual practitioners. Science that is robust and reproducible depends on understanding these dynamics so that institutional programs for improvement can specifically target them.
PREPRINT-CONTENT 0 Reads 0 Citations Open science and modified funding lotteries can impede the natural selection of bad science Paul Smaldino, Matthew Adam Turner, Pablo Andrés Contreras K... Published: 28 January 2019
doi: 10.31219/
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Assessing scientists using exploitable metrics can lead to the degradation of research methods even without any strategic behavior on the part of individuals, via ``the natural selection of bad science." Institutional incentives to maximize metrics like publication quantity and impact drive this dynamic. Removing these incentives is necessary, but institutional change is slow. However, recent developments suggest possible solutions with more rapid onsets. These include what we call open science improvements, which can reduce publication bias and improve the efficacy of peer review. In addition, there have been increasing calls for funders to move away from prestige- or innovation-based approaches in favor of lotteries. We investigated whether such changes are likely to improve the reproducibility of science even in the presence of persistent incentives for publication quantity through computational modeling. We found that modified lotteries, which allocate funding randomly among proposals that pass a threshold for methodological rigor, effectively reduce the rate of false discoveries, particularly when paired with open science improvements that increase the publication of negative results and improve the quality of peer review. In the absence of funding that targets rigor, open science improvements can still reduce false discoveries in the published literature but are less likely to improve the overall culture of research practices that underlie those publications.
PREPRINT-CONTENT 0 Reads 0 Citations Enhancing and accelerating social science via automation: Challenges and opportunities Tal Yarkoni, Dean Eckles, James Heathers, Margaret Levenstei... Published: 25 January 2019
doi: 10.31235/
DOI See at publisher website
Article 0 Reads 0 Citations Cultural evolution of categorization Pablo Andrés Contreras Kallens, Rick Dale, Paul E. Smaldino Published: 01 December 2018
Cognitive Systems Research, doi: 10.1016/j.cogsys.2018.08.026
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PREPRINT-CONTENT 0 Reads 0 Citations Niche Diversity Can Explain Cross-Cultural Differences in Personality Structure Paul Smaldino, Aaron Lukaszewski, Christopher Von Rueden, Mi... Published: 26 September 2018
doi: 10.31234/
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The structure of personality refers to the covariation among specific behavioral patterns in a population. Statistically derived models of personality---such as the Big Five or HEXACO models---usually assume that the covariance structure of personality characteristics is a human universal. Cross-cultural studies, however, have challenged this view, finding that less complex societies exhibit stronger covariation among behavioral characteristics, resulting in fewer derived personality factors. To explain these results, we propose the niche diversity hypothesis, which predicts that a greater diversity of social and ecological niches elicits a more diverse set of multivariate behavioral profiles, and hence lower trait covariance, at the population level. We formalize this hypothesis as a computational model in which individuals assort into niches, which influence their behavioral traits. We find that the model provides strong support for the niche diversity hypothesis and reproduces empirical results from recent cross-cultural studies. This work provides a general explanation for differences in personality structure between populations in both humans and other animals, and also produces several new empirical predictions. It also suggests a radical reimagining of personality trait research: instead of reifying statistical descriptions of manifest personality structures, research should focus more attention on modeling their underlying causes.
Article 0 Reads 1 Citation Sigmoidal Acquisition Curves Are Good Indicators of Conformist Transmission Paul E. Smaldino, Lucy M. Aplin, Damien R. Farine Published: 18 September 2018
Scientific Reports, doi: 10.1038/s41598-018-30248-5
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
The potential for behaviours to spread via cultural transmission has profound implications for our understanding of social dynamics and evolution. Several studies have provided empirical evidence that local traditions can be maintained in animal populations via conformist learning (i.e. copying the majority). A conformist bias can be characterized by a sigmoidal relationship between a behavior’s prevalence in the population and an individual’s propensity to adopt that behavior. For this reason, the presence of conformist learning in a population is often inferred from a sigmoidal acquisition curve in which the overall rate of adoption for the behavior is taken as the dependent variable. However, the validity of sigmoidal acquisition curves as evidence for conformist learning has recently been challenged by models suggesting that such curves can arise via alternative learning rules that do not involve conformity. We review these models, and find that the proposed alternative learning mechanisms either rely on faulty or unrealistic assumptions, or apply only in very specific cases. We therefore recommend that sigmoidal acquisition curves continue to be taken as evidence for conformist learning. Our paper also highlights the importance of understanding the generative processes of a model, rather than only focusing solely on the patterns produced. By studying these processes, our analysis suggests that current practices by empiricists have provided robust evidence for conformist transmission in both humans and non-human animals.