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On disciplinary fragmentation and scientific progress.

Balietti S, Mäs M, Helbing D - PLoS ONE (2015)

Bottom Line: Strikingly, there is no effect in the opposite causal direction.What is more, our results shows that at the heart of the mechanisms driving scientific progress we find (i) social interactions, and (ii) peer disagreement.We discuss model's implications for the design of social institutions fostering interdisciplinarity and participation in science.

View Article: PubMed Central - PubMed

Affiliation: Professorship of Computational Social Science, ETH Zurich, Switzerland.

ABSTRACT
Why are some scientific disciplines, such as sociology and psychology, more fragmented into conflicting schools of thought than other fields, such as physics and biology? Furthermore, why does high fragmentation tend to coincide with limited scientific progress? We analyzed a formal model where scientists seek to identify the correct answer to a research question. Each scientist is influenced by three forces: (i) signals received from the correct answer to the question; (ii) peer influence; and (iii) noise. We observed the emergence of different macroscopic patterns of collective exploration, and studied how the three forces affect the degree to which disciplines fall apart into divergent fragments, or so-called "schools of thought". We conducted two simulation experiments where we tested (A) whether the three forces foster or hamper progress, and (B) whether disciplinary fragmentation causally affects scientific progress and vice versa. We found that fragmentation critically limits scientific progress. Strikingly, there is no effect in the opposite causal direction. What is more, our results shows that at the heart of the mechanisms driving scientific progress we find (i) social interactions, and (ii) peer disagreement. In fact, fragmentation is increased and progress limited if the simulated scientists are open to influence only by peers with very similar views, or when within-school diversity is lost. Finally, disciplines where the scientists received strong signals from the correct answer were less fragmented and experienced faster progress. We discuss model's implications for the design of social institutions fostering interdisciplinarity and participation in science.

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(A) The effect of the strength of social influence α on the average number of clusters, and (B) the average distance from the ground truth at time point 2,000.The effect of α is particularly strong for R = 0.03, and negligible for R = 0.3. In the case of a small radius of interaction, the U-shaped relationship depicted in Panel B is due to the high level of cohesiveness of the clusters at the beginning of the simulation, when social influence is very high. Error bars represent standard errors of the mean.[R = (0.03; 0.3), α = (0.01 − 0.99), τ = 1, σ = 0.01, ε = 0.1]
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pone.0118747.g005: (A) The effect of the strength of social influence α on the average number of clusters, and (B) the average distance from the ground truth at time point 2,000.The effect of α is particularly strong for R = 0.03, and negligible for R = 0.3. In the case of a small radius of interaction, the U-shaped relationship depicted in Panel B is due to the high level of cohesiveness of the clusters at the beginning of the simulation, when social influence is very high. Error bars represent standard errors of the mean.[R = (0.03; 0.3), α = (0.01 − 0.99), τ = 1, σ = 0.01, ε = 0.1]

Mentions: Fig. 5 informs about the effects of the second social-influence parameter of our model, the strength α of social influence. Panel A shows that the strength of social influence had an effect when the influence radius was small (R = 0.03). Under this condition, very weak social influence resulted in a higher degree of fragmentation. For instance, when α adopted a value of 0.01, the smallest value that we studied, we counted 30.2 clusters on average. When α was increased to 0.1, we counted only 15.9 clusters on average. However, when social influence was stronger than α = 0.25, a further increase in social influence strength hardly affected fragmentation. The populations with a large influence radius (R = 0.3) always reached a consensus within the 2.000 simulation events. However, when fragmentation was measured earlier in the simulation process (e.g. t = 100, not shown here) a similar effect obtains as for the simulations with the small influence radius.


On disciplinary fragmentation and scientific progress.

Balietti S, Mäs M, Helbing D - PLoS ONE (2015)

(A) The effect of the strength of social influence α on the average number of clusters, and (B) the average distance from the ground truth at time point 2,000.The effect of α is particularly strong for R = 0.03, and negligible for R = 0.3. In the case of a small radius of interaction, the U-shaped relationship depicted in Panel B is due to the high level of cohesiveness of the clusters at the beginning of the simulation, when social influence is very high. Error bars represent standard errors of the mean.[R = (0.03; 0.3), α = (0.01 − 0.99), τ = 1, σ = 0.01, ε = 0.1]
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4366147&req=5

pone.0118747.g005: (A) The effect of the strength of social influence α on the average number of clusters, and (B) the average distance from the ground truth at time point 2,000.The effect of α is particularly strong for R = 0.03, and negligible for R = 0.3. In the case of a small radius of interaction, the U-shaped relationship depicted in Panel B is due to the high level of cohesiveness of the clusters at the beginning of the simulation, when social influence is very high. Error bars represent standard errors of the mean.[R = (0.03; 0.3), α = (0.01 − 0.99), τ = 1, σ = 0.01, ε = 0.1]
Mentions: Fig. 5 informs about the effects of the second social-influence parameter of our model, the strength α of social influence. Panel A shows that the strength of social influence had an effect when the influence radius was small (R = 0.03). Under this condition, very weak social influence resulted in a higher degree of fragmentation. For instance, when α adopted a value of 0.01, the smallest value that we studied, we counted 30.2 clusters on average. When α was increased to 0.1, we counted only 15.9 clusters on average. However, when social influence was stronger than α = 0.25, a further increase in social influence strength hardly affected fragmentation. The populations with a large influence radius (R = 0.3) always reached a consensus within the 2.000 simulation events. However, when fragmentation was measured earlier in the simulation process (e.g. t = 100, not shown here) a similar effect obtains as for the simulations with the small influence radius.

Bottom Line: Strikingly, there is no effect in the opposite causal direction.What is more, our results shows that at the heart of the mechanisms driving scientific progress we find (i) social interactions, and (ii) peer disagreement.We discuss model's implications for the design of social institutions fostering interdisciplinarity and participation in science.

View Article: PubMed Central - PubMed

Affiliation: Professorship of Computational Social Science, ETH Zurich, Switzerland.

ABSTRACT
Why are some scientific disciplines, such as sociology and psychology, more fragmented into conflicting schools of thought than other fields, such as physics and biology? Furthermore, why does high fragmentation tend to coincide with limited scientific progress? We analyzed a formal model where scientists seek to identify the correct answer to a research question. Each scientist is influenced by three forces: (i) signals received from the correct answer to the question; (ii) peer influence; and (iii) noise. We observed the emergence of different macroscopic patterns of collective exploration, and studied how the three forces affect the degree to which disciplines fall apart into divergent fragments, or so-called "schools of thought". We conducted two simulation experiments where we tested (A) whether the three forces foster or hamper progress, and (B) whether disciplinary fragmentation causally affects scientific progress and vice versa. We found that fragmentation critically limits scientific progress. Strikingly, there is no effect in the opposite causal direction. What is more, our results shows that at the heart of the mechanisms driving scientific progress we find (i) social interactions, and (ii) peer disagreement. In fact, fragmentation is increased and progress limited if the simulated scientists are open to influence only by peers with very similar views, or when within-school diversity is lost. Finally, disciplines where the scientists received strong signals from the correct answer were less fragmented and experienced faster progress. We discuss model's implications for the design of social institutions fostering interdisciplinarity and participation in science.

Show MeSH
Related in: MedlinePlus