Quantifying knowledge exchange in R&D networks: a data-driven model
                Authored by Frank Schweitzer, Mario V Tomasello, Claudio J Tessone, Giacomo Vaccario
                
                    Date Published: 2018
                
                
                    DOI: 10.1007/s00191-018-0569-1
                
                
                    Sponsors:
                    
                        European Union
                        
                
                
                    Platforms:
                    
                        No platforms listed
                    
                
                
                    Model Documentation:
                    
                        Other Narrative
                        
                
                
                    Model Code URLs:
                    
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                Abstract
                We propose a model that reflects two important processes in R\&D
activities of firms, the formation of R\&D alliances and the exchange of
knowledge as a result of these collaborations. In a data-driven
approach, we analyze two large-scale data sets, extracting unique
information about 7500 R\&D alliances and 5200 patent portfolios of
firms. These data are used to calibrate the model parameters for network
formation and knowledge exchange. We obtain probabilities for incumbent
and newcomer firms to link to other incumbents or newcomers able to
reproduce the topology of the empirical R\&D network. The position of
firms in a knowledge space is obtained from their patents using two
different classification schemes, IPC in eight dimensions and
ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange
assumes that collaborating firms approach each other in knowledge space
at a rate mu for an alliance duration tau. Both parameters are obtained
in two different ways, by comparing knowledge distances from simulations
and empirics and by analyzing the collaboration efficiency . This is a
new measure that takes in account the effort of firms to maintain
concurrent alliances, and is evaluated via extensive computer
simulations. We find that R\&D alliances have a duration of around two
years and that the subsequent knowledge exchange occurs at a very low
rate. Hence, a firm's position in the knowledge space is rather a
determinant than a consequence of its R\&D alliances. From our
data-driven approach we also find model configurations that can be both
realistic and optimized with respect to the collaboration efficiency .
Effective policies, as suggested by our model, would incentivize shorter
R\&D alliances and higher knowledge exchange rates.
                
Tags
                
                    Agent-based model
                
                    Evolution
                
                    Performance
                
                    Innovation
                
                    Dynamics
                
                    Efficiency
                
                    Knowledge exchange
                
                    partnerships
                
                    Impact
                
                    Firm
                
                    Industry
                
                    Alliances
                
                    Strategic alliances
                
                    Biotechnology
                
                    Inter-firm network
                
                    R\&d alliances
                
                    Patents
                
                    Interorganizational collaboration
                
                    Innovation
networks
                
                    Structural holes
                
                    Local search