Limits...
On disciplinary fragmentation and scientific progress.

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

Bottom Line: 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.

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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Ideal typical runs showing different patterns of consensus and level of scientific progress at four time steps: t = 1, 200, 1000, 2000.Panel A shows a steady trend towards consensus ending up almost perfectly on the ground truth [R = 0.3, α = 0.5, τ = 0.9, σ = 0.01, ε = 0.1]. In Panel B, the same process of convergence ends up far away from truth [R = 0.3, α = 0.5, τ = 10, σ = 0.01, ε = 0.1]. Panel C shows the emergence of different schools of thought, that gradually merge together in proximity of the truth [R = 0.06, α = 0.9, τ = 3, σ = 0.02, ε = 0.25]. Panel D shows the emergence of many small clusters that persist over time[R = 0.03, α = 0.9, τ = 1, σ = 0.01, ε = 0.1]. Finally, in Panel E the process of school formation is so slow that even at the end of the simulation a relevant share of agents is still isolated [R = 0.03, α = 0.01, τ = 1, σ = 0.01, ε = 0.1].
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pone.0118747.g002: Ideal typical runs showing different patterns of consensus and level of scientific progress at four time steps: t = 1, 200, 1000, 2000.Panel A shows a steady trend towards consensus ending up almost perfectly on the ground truth [R = 0.3, α = 0.5, τ = 0.9, σ = 0.01, ε = 0.1]. In Panel B, the same process of convergence ends up far away from truth [R = 0.3, α = 0.5, τ = 10, σ = 0.01, ε = 0.1]. Panel C shows the emergence of different schools of thought, that gradually merge together in proximity of the truth [R = 0.06, α = 0.9, τ = 3, σ = 0.02, ε = 0.25]. Panel D shows the emergence of many small clusters that persist over time[R = 0.03, α = 0.9, τ = 1, σ = 0.01, ε = 0.1]. Finally, in Panel E the process of school formation is so slow that even at the end of the simulation a relevant share of agents is still isolated [R = 0.03, α = 0.01, τ = 1, σ = 0.01, ε = 0.1].

Mentions: The interplay of the decisions that the simulated scientists make can generate very different patterns of disciplinary fragmentation and scientific progress. For illustration, Fig. 2 shows five very different yet typical simulation runs. In each run one can observe a distinct collective dynamic, even though all five runs started with a very similar random initial distributions of individual views and approaches. The run shown in Panel A was characterized by a very strong tendency to converge onto the ground truth at the center of the epistemic space. In this run, fragmentation was very low and progress was quick. In the run shown in Panel B, agents’ views also converged, but the emergent consensus was substantially further apart from the ground truth. Thus, progress was more limited in this run. Panel C shows a run where we observed fragmentation. That is, several internally homogeneous schools of thought with mutually different views emerged. As the simulation progressed, however, the schools merged and collectively progressed towards the ground truth. Similar dynamics were observed in the run shown in Panel D. However, the state of fragmentation was much more robust and progress slower. Finally, Panel E illustrates a run where the fragmentation was even stronger and almost no progress occurred. Additional patterns of collective exploration are shown in S1 Model Dynamics Video.


On disciplinary fragmentation and scientific progress.

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

Ideal typical runs showing different patterns of consensus and level of scientific progress at four time steps: t = 1, 200, 1000, 2000.Panel A shows a steady trend towards consensus ending up almost perfectly on the ground truth [R = 0.3, α = 0.5, τ = 0.9, σ = 0.01, ε = 0.1]. In Panel B, the same process of convergence ends up far away from truth [R = 0.3, α = 0.5, τ = 10, σ = 0.01, ε = 0.1]. Panel C shows the emergence of different schools of thought, that gradually merge together in proximity of the truth [R = 0.06, α = 0.9, τ = 3, σ = 0.02, ε = 0.25]. Panel D shows the emergence of many small clusters that persist over time[R = 0.03, α = 0.9, τ = 1, σ = 0.01, ε = 0.1]. Finally, in Panel E the process of school formation is so slow that even at the end of the simulation a relevant share of agents is still isolated [R = 0.03, α = 0.01, τ = 1, σ = 0.01, ε = 0.1].
© Copyright Policy
Related In: Results  -  Collection

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

pone.0118747.g002: Ideal typical runs showing different patterns of consensus and level of scientific progress at four time steps: t = 1, 200, 1000, 2000.Panel A shows a steady trend towards consensus ending up almost perfectly on the ground truth [R = 0.3, α = 0.5, τ = 0.9, σ = 0.01, ε = 0.1]. In Panel B, the same process of convergence ends up far away from truth [R = 0.3, α = 0.5, τ = 10, σ = 0.01, ε = 0.1]. Panel C shows the emergence of different schools of thought, that gradually merge together in proximity of the truth [R = 0.06, α = 0.9, τ = 3, σ = 0.02, ε = 0.25]. Panel D shows the emergence of many small clusters that persist over time[R = 0.03, α = 0.9, τ = 1, σ = 0.01, ε = 0.1]. Finally, in Panel E the process of school formation is so slow that even at the end of the simulation a relevant share of agents is still isolated [R = 0.03, α = 0.01, τ = 1, σ = 0.01, ε = 0.1].
Mentions: The interplay of the decisions that the simulated scientists make can generate very different patterns of disciplinary fragmentation and scientific progress. For illustration, Fig. 2 shows five very different yet typical simulation runs. In each run one can observe a distinct collective dynamic, even though all five runs started with a very similar random initial distributions of individual views and approaches. The run shown in Panel A was characterized by a very strong tendency to converge onto the ground truth at the center of the epistemic space. In this run, fragmentation was very low and progress was quick. In the run shown in Panel B, agents’ views also converged, but the emergent consensus was substantially further apart from the ground truth. Thus, progress was more limited in this run. Panel C shows a run where we observed fragmentation. That is, several internally homogeneous schools of thought with mutually different views emerged. As the simulation progressed, however, the schools merged and collectively progressed towards the ground truth. Similar dynamics were observed in the run shown in Panel D. However, the state of fragmentation was much more robust and progress slower. Finally, Panel E illustrates a run where the fragmentation was even stronger and almost no progress occurred. Additional patterns of collective exploration are shown in S1 Model Dynamics Video.

Bottom Line: 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.

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