What Composition of High-Energy Physics Collaborations is Epistemically Optimal?

ABSTRACT

Computer simulations have recently come to the fore as a crucial tool for studying epistemic dynamics of scientific collectives. One pressing issue in big-ticket, long-term, and crowded particle physics research is the optimal organization of collaborations. This research developed a computation model of collaborations in high-energy physics and performed simulations to investigate the epistemic efficiency of groups and its dependence on the size of such groups and their composition: namely, percentages of pure, partially theoretically competent, and fully theoretically competent experimentalists. The present study reveals that in both small (100-member) and large (3000-member) groups, epistemic payoff as the measure of epistemic efficiency of collaborations is , generally, positively correlated with an increase in both the number of theoretically fully competent and pure (incompetent) experimentalists. We are less certain of such conclusion in the case of the small collaborations. Although the subcommunity of experimentalists who are partially expert is not found to be immediately epistemically beneficial for collaborations, nevertheless they also crucially serve as a transitional community between the theorists and the experimentalists. Predicated on the toy model simulations, I suggest that institutions should provide measures to assist members of the latter subgroup in progressing toward developing a full theoretical expertise.

KEYWORDS:

• Epistemic dynamics
• high-Energy physics
• collaborations
• optimal organization

Acknowledgements

The author is grateful to the organizers and audience of the International Conference: Philosophy of Scientific Experimentation 5 (PSX5) held on 22 – 23 September 2016 at University of Belgrade, Serbia as well as of the 8th Models and Simulations conference (MS8) held at Columbia, South Carolina on 15 – 17 March 2018 for useful discussions. The author is indebted to the Editor and two anonymous reviewers for their careful reading of the manuscript and their many insightful comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. The terms subjective reductionists, objective reductionists and credulists are introduced in (Zollman 2014). Epistemic strategies of these groups for the case of high-energy physics are explained in this paper.

2. Computer simulation allows one to quickly and easily vary the parameters, while analytical solutions, when they exist, allow the verification of simulations. It seems noteworthy that the equations (1)-(6) can be analytically rewritten for all the models presented in the paper as $P=N_C\ast K_t+N_{S}R\ast \left(2\ast K_t\ast K_{S}R-K_t-K_{S}R+1\right)+N_{O}R\ast K_{O}R$. In the case of the Model 2 payoff, the equation can be simplified to P(Model 2) = $0.9\ast N_C+0.58\ast N_{S}R+0.8\ast N_{O}R$, and in the case of the Model 3 payoff to $P\left(Model3\right)=0.9\ast N_C+0.74\ast N_{SR}+0.6\ast N_{OR}$. Assuming N_OR = 100 and requiring the equality of payoffs for both models (the condition of intersection for respective curves in Figure 1), one can obtain N_SR = 125 and N_C = 2775. The latter value is in a good agreement with the one obtained with simulation.

Additional information

Funding

Fermi National Accelerator Laboratory is operated by the Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

Notes on contributors

Vitaly Pronskikh

Vitaly Pronskikh has worked as a researcher at the Fermi National Accelerator Laboratory in the U.S. since 2010. He is an associate member of the Center for Philosophy of Science at the University of Pittsburgh. He previously worked as a senior scientist at the Joint Institute for Nuclear Research (JINR) in Dubna from 1994 to 2010. In 2002, he was awarded a Bruno Pontecorvo Fellowship for Young Scientists from JINR. He received a PhD in philosophy of science and technology from Lomonosov Moscow State University in 2017 and a PhD in nuclear and particle physics from JINR in 2005. In 1994, he obtained an MSc in nuclear technology from Saint Petersburg State Institute of Technology in Russia. He publishes and presents on fundamental and applied nuclear physics, nuclear technologies, and particle physics as well as the history and philosophy of science, ethics of science and technology, and the philosophy of computer modeling and simulations.