When should a scientific community be cognitively diverse? This article presents a model for studying how the heterogeneity of learning heuristics used by scientist agents affects the epistemic efficiency of a scientific community. By extending the epistemic landscapes modeling approach introduced by Weisberg and Muldoon, the article casts light on the micro-mechanisms mediating cognitive diversity, coordination, and problem-solving efficiency. The results suggest that social learning and cognitive diversity produce epistemic benefits only when the epistemic community is faced with problems of sufficient difficulty.
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This same aspect of cognitive diversity is also the topic of the subsequent epistemic landscape models introduced by Thoma (2015) and Alexander et al. (2015). Zollman (2010), in contrast, deals with a different aspect of diversity. In the context of theory choice, he examines how (a) the flow of information in social networks and (b) the strength of agents’ prior degrees of belief influence the emergence of consensus in theory choice.
For a Python replication of Weisberg and Muldoon’s model, and the source code for the simulations in this article, see the repository at https://github.com/samulipo/broadcasting/. Simulations were conducted with n = 50 for each data point. Error bars (when shown) stand for one standard deviation in sample.
Consequently, the average epistemic significance of the population at a particular time (used in Sect. 2.3) is the change in W over one time step, scaled by a constant.
Weisberg and Muldoon (2009, p. 232), suggest implementing such interaction between agents and landscape as a possible extension of their model. Likewise, Thoma (2015, footnote 7) brings up the idea of a modified model where revisiting patches uncovers further epistemic significance, but she does not develop the idea further.
Simulation experiments were also run with agents who, instead of aiming for maximum significance level, aim to maximize the epistemic work done over a future time period, and yet more sophisticated ones who try to take landscape depletion into account by exponentially discounting for distant payoffs based on \(\lambda \). Such decision rules result in a field of vision delimited by a surface of revolution drawn by a non-linear function. Careful analysis of such situations must be left as a task for future work, but in initial experimentation the differences in the shape of the cone did not lead to qualitative changes in the results.
However, as the value of \(\lambda \) becomes smaller, harvesting epistemic significance from a patch becomes slower, and movement on the landscape becomes relatively less costly. Consequently, the choice of a search heuristic becomes less critical. Real research topics with small lambdas would be ones where changing one’s approach is relatively quick compared to the time it takes to produce results by using a chosen approach.
Thanks to the anonymous referee for insisting on the use of alternative measures of epistemic success.
Due to the nature of the model (see the discussion in Sect. 5), the qualitative results from the model should not depend on the choice of particular point values for the parameters. The scaling of the parameter space was chosen mainly for convenience and for its continuity with the EL model.
Animations of individual simulation runs with different kinds of populations can be found in the online supplementary materials.
As the ruggedness of the landscape increases even more, local search heuristics generally become less and less useful, and even the diverse communities make little progress. Such landscapes can be seen to represent research problems beyond the cognitive capacities of the scientist agents, where attaining significant results becomes increasingly a matter of luck.
As document 1 in the supplementary materials shows, these results hold remarkably well across beta values ranging from 50 to 300, and across different times of measurement.
By herding, I refer to undesirable behavior where agents do what others do even in situations where they should be relying on their own information (Banerjee 1992).
Measuring epistemic progress\(^*\) (see supplementary material, documents 3 and 4) suggests a further advantage of diversity. Unlike populations of social learners, given enough time, diverse populations and populations of individual learners do not leave behind unexamined significant patches on the landscape. This is due to their local search strategy: Agents conducting individual learning only leave a neighborhood of patches once all its patches have been depleted. Hence, in this way, diversity can make the population of scientists more pedantic in its work.
As full results reported in document 4 in the supplementary materials confirm, the ordering in Fig. 5 is stable across the whole range of examined \(\beta \) values. However, for the higher ruggedness values (\(\beta \in [200,300]\)), adding some extra individual learners can lead to a small additional payoff in last stages of the 1000-round simulation run.
The interaction effects between task difficulty and the distribution of social learning thresholds shows in Table 2, where no column dominates the others. For example, at different values of \(\lambda \), pure social learning can be the best, second, or even the worst learning heuristic among the three.
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I would like to thank the anonymous referees for their useful comments and suggestions on earlier drafts of the paper. I am also thankful to Manuela Fernández Pinto, Marion Godman, Jaakko Kuorikoski, Otto Lappi, Caterina Marchionni, Carlo Martini, and Petri Ylikoski for helpful discussions about the paper and about epistemic landscapes modeling in general. The paper also benefited from comments by the participants at the Agent-based Models in Philosophy conference at LMU Munich, TINT brown bag seminar, and the cognitive science research seminar at University of Helsinki. This research has been financially supported by the Academy of Finland and the University of Helsinki.
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Pöyhönen, S. Value of cognitive diversity in science. Synthese 194, 4519–4540 (2017). https://doi.org/10.1007/s11229-016-1147-4
- Social epistemology
- Social learning
- Division of cognitive labor