Explore your experimental designs and theories before you exploit them!

In many areas of the social and behavioral sciences, the nature of the experiments and theories that best capture the underlying constructs are themselves areas of active inquiry. Integrative experiment design risks being prematurely exploitative, hindering exploration of experimental paradigms and of diverse theoretical accounts for target phenomena.

Beyond collective intelligence: Collective adaptation.

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.

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.

Can we detect conditioned variation in political speech? two kinds of discussion and types of conversation.

Previous work has demonstrated that certain speech patterns vary systematically between sociodemographic groups, so that in some cases the way a person speaks is a valid cue to group membership. Our work addresses whether or not participants use these linguistic cues when assessing a speaker's likely political identity. We use a database of speeches by U.S. Congressional representatives to isolate words that are statistically diagnostic of a speaker's party identity. In a series of four studies, we demonstrate that participants' judgments track variation in word usage between the two parties more often than chance, and that this effect persists even when potentially interfering cues such as the meaning of the word are controlled for. Our results are consistent with a body of literature suggesting that humans' language-related judgments reflect the statistical distributions of our environment.

Think of the consequences: A decade of discourse about same-sex marriage.

Approaching issues through the lens of nonnegotiable values increases the perceived intractability of debate (Baron & Spranca in Organizational Behavior and Human Decision Processes, 70, 1-16, 1997), while focusing on the concrete consequences of policies instead results in the moderation of extreme opinions (Fernbach, Rogers, Fox, & Sloman in Psychological Science, 24, 939-946, 2013) and a greater likelihood of conflict resolution (Baron & Leshner in Journal of Experimental Psychology: Applied, 6, 183-194, 2000). Using comments on the popular social media platform Reddit from January 2006 until September 2017, we showed how changes in the framing of same-sex marriage in public discourse relate to changes in public opinion. We used a topic model to show that the contributions of certain protected-values-based topics to the debate (religious arguments and freedom of opinion) increased prior to the emergence of a public consensus in support of same-sex marriage (Gallup, 2017), and declined afterward. In contrast, the discussion of certain consequentialist topics (the impact of politicians' stance and same-sex marriage as a matter of policy) showed the opposite pattern. Our results reinforce the meaningfulness of protected values and consequentialism as relevant dimensions for describing public discourse and highlight the usefulness of unsupervised machine-learning methods in tackling questions about social attitude change.