on disciplinary fragmentation and scientific progress stefano balietti, michael mäs, and dirk...

On Disciplinary Fragmentation and Scientific Progress

Stefano Balietti, Michael Mäs, and Dirk Helbing

ETH Zurich, Chair of Sociology in particular Modeling and Simulations

Social Simulation Conference – Barcelona, 05-09-2014

ETH Zürich | Stefano Balietti | [email protected] | SOMS Chair of Sociology, Modeling and Simulation


Research Questions

Why are some disciplines more fragmented into conflicting schools of thoughts than others ?

Why do disciplinary fragmentation and limited scientific progress seem to correlate ?


Physics: a Path of Unification



Isaac Newton(1642 - 1727)

Unified celestial and terrestrial forceswith the law of gravity.



James Maxwell(1831 – 1879)

Unified electricity and magnetism in one single force calledelectromagnetism.



Albert Einstein(1879 - 1955)

Unified the Newtonian theory ofgravitation and his special relativitywith the theory of general relativity.



Steven Weinberg(1933)Unified, on a subatomic level,

the strong force, and the weak force,and the electromagnetic force in once single model calledthe Standard Model.


The Social Science Archipelago


The Social Science Archipelago

Science is composed of an archipelago of typically smallish and highly cohesive communities (Liljeros 2010).

High between-, low within-group variance. Different social norms apply to different communities.


Fragmentation in Social Sciences

H. Gintis @ Game Theory and Society Zurich, 2011. “Analytical Foundation of Sociological Game Theory”

With the sole exception of economics, the social sciences are still missing a core theory that everybody acknowledges.

De Langhe R (2009) Mainstream economics: Searching where the light is. Journal of Post Keynesian Economics 32: 137–150.


Research Questions

Why are some disciplines more fragmented into conflicting schools of thoughts than others ?

Why do disciplinary fragmentation and limited scientific progress seem to correlate ?


References

Vicsek et Al. (1995)

“Novel type of phase transition in a system of Self-Driven particles”. Physical Review Letters 75: 1226–1229

Hegselmann R., Krause U. (2006)

“Truth and cognitive division of labour: First steps towards a computer aided social epistemology.” Journal of Artificial Societies and Social Simulation 9: 1–28.


Model Description

Scientists explore a 2-dimensional epistemic space of possible answers to a research question

The movement represents the approach The position represents the current view

Simultaneously exposed to 3 forces:

Attraction towards the ground truth Social influence of related opinions Randomness (Noise)


Formal Model


Formal Model

Position Noise Angular Noise


Model parameters

Ground Truth– Strength of attraction τ

Social Influence: Radius of interaction R Strength of social influence α

Noise:

Intensity position noise ε Intensity angular noise σ


Ideal-Typical Model Outcomes


Model Dynamics Explained

Agents move randomly in the epistemic space



Agents enter each other interaction areas



Agents approaches are updated by social influence



New approach is weighted average of own approachand average approach within interaction radius



New approach has a new direction and intensity



Approach is updated immediately



Update of approaches continues for other agents



B and C are both within A's interaction radius



A's approach was reduced by social influence



Also B's velocity vector is reduced by social influence



B and A form a cluster, C leaves interaction areas



Approaches become increasingly more similar



Ground truth is now introduced



Agents are pulled back towards the ground truth ...



… until the velocity of the approaches equalsthe distance from ground truth



Position noise is now introduced



Perturbs the relative position of agents in the cluster



but on average the distance from truth stay the same



Angular noise is now introduced



Perturbs the direction of agents' approaches



Social influence reduce differences in approachesand agents move closer to ground truth


Structural Variables

Strong signal from ground truth (++) Large interaction radius (++) Angular noise (++) Strong social influence (+)

Fragmentation (-) Clustering (-)


Correlation Fragmentation and Scientific Progress



Social interactions leads to clusters

Clusters move agents closer to truth

Angular noise keeps agents continuously slightly mis-aligned

Diversity of approaches is preserved



Social interactions leads to clusters

Clusters move agents closer to truth

Angular noise keeps agents continuously slightly mis-aligned

Diversity of approaches is preserved

Social interactions and peer disagreement are two key mechanisms to promote progress


Simulation Experiment 2

Randomly assigned agents to c clusters Clusters were placed equally spaced on a circle of

radius d with the ground truth in the center

Varied c to manipulate the degree of fragmentation

Measured time necessary to form consensus on the ground truth (75% within radius 0.05 units)

Varied d to manipulated the degree of progress Measured the average number of clusters at

consensus share 50%


Simulation Experiment 2


Fragmentation on Progress

Fragmentation hampers scientific progress


Progress on Fragmentation

Scientific progress has no effect on fragmentation


Fragmentation and Progress

Fragmentation slows down scientific progress Progress does not have an effect on

fragmentation




fragmentation

Both structural variables and fragmentation affect scientific progress




fragmentation


It might be that the structural variables only affect fragmentation, that in turn slows down progress




fragmentation


It might be that the structural variables only affect fragmentation, that in turn slows down progress

To test this hypothesis we performed Baron and Kenny (1986) mediation analysis


Mediation Analysis Baron and Kenny (1986)

Step 1: Regress progress on structural variables (R, σ, and τ )

Step 2: Regress fragmentation on structural variables (R, σ, and τ )

Step 3: Regress progress on fragmentation and structural variables (R, σ, and τ )

If the statistical effect is smaller in Step 3. than in Step 1. the parameter is partially mediated

If the statistical effect is even insignificant, the parameter is completely mediated


Mediation Analysis Results

The effect of all structural variables on progress is at least partially mediated by fragmentation

The angular noise parameter σ is completed mediated by fragmentation.


Mediation Analysis Results

The effect of all structural variables on progress is at least partially mediated by fragmentation

The angular noise parameter σ is completed mediated by fragmentation

Therefore only the length of the radius of influence R, and the strength of attraction to ground truth τ are actually responsible for the correlation between disciplinary fragmentation and scientific progress.


Research Questions Why are some disciplines more fragmented into

conflicting schools of thoughts than others ? Why do disciplinary fragmentation and limited

scientific progress seem to correlate ?


Thank You Very Much

“On Fragmentation and Scientific Progress”

Stefano Balietti, ETH Zurich – [email protected]

Chair of Sociology, in particular Modeling and Simulations

on disciplinary fragmentation and scientific progress stefano balietti, michael mäs, and dirk...

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