To Stand on the Shoulders of Giants: Should We Protect Initial Discoveries in Multi-Agent Exploration?

TL;DR

This paper analyzes whether protecting initial discoveries or sharing knowledge fosters better exploration in research, using game theory and lab experiments, revealing that open sharing surprisingly encourages more effort despite theoretical expectations.

Contribution

It provides a game theoretic framework comparing protection versus sharing of discoveries and empirically tests these strategies in controlled experiments.

Findings

No protection leads to more overall effort, contrary to theory.

Sharing knowledge increases exploration effort in practice.

Experimental results align with cognitive biases rather than economic predictions.

Abstract

Exploring new ideas is a fundamental aspect of research and development (R\&D), which often occurs in competitive environments. Most ideas are subsequent, i.e. one idea today leads to more ideas tomorrow. According to one approach, the best way to encourage exploration is by granting protection on discoveries to the first innovator. Correspondingly, only the one who made the first discovery can use the new knowledge and benefit from subsequent discoveries, which in turn should increase the initial motivation to explore. An alternative approach to promote exploration favors the \emph{sharing of knowledge} from discoveries among researchers allowing explorers to use each others' discoveries to develop further knowledge, as in the open-source community. With no protection, all explorers have access to all existing discoveries and new directions are explored faster. We present a game theoretic analysis of an abstract research-and-application game which clarifies the expected advantages and disadvantages of the two approaches under full information. We then compare the theoretical predictions with the observed behavior of actual players in the lab who operate under partial information conditions in both worlds. Our main experimental finding is that the no protection approach leads to \emph{more} investment efforts overall, in contrast to theoretical prediction and common economic wisdom, but in line with a familiar cognitive bias known as `underweighting of rare events'.