R&D Subsidization Effect and Network Centralization: Evidence from an Agent-Based Micro-Policy Simulation

被引:2
作者
Angelini, Pierpaolo [1 ]
Cerulli, Giovanni [2 ]
Cecconi, Federico [3 ]
Miceli, Maria-Augusta [4 ]
Poti, Bianca [2 ]
机构
[1] Ist Ric Econ & Sociali, Via Santa Teresa 23, I-00198 Rome, Italy
[2] CNR, Res Inst Sustainable Econ Growth, Via Taurini 19, I-00185 Rome, Italy
[3] CNR, Inst Cognit Sci & Technol, Lab Agent Based Social Simulat, Via Palestro 32, I-00185 Rome, Italy
[4] Sapienza Univ Rome, Dept Econ & Law, Via Castro Laurenziano 20, I-00161 Rome, Italy
来源
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION | 2017年 / 20卷 / 04期
关键词
R&D Policy; Networks; Complexity; Social Simulation; FRAMEWORK; DYNAMICS; COLLABORATION; DISTRIBUTIONS; INNOVATION; EMERGENCE; SUBSIDIES; GROWTH;
D O I
10.18564/jasss.3494
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
This paper presents an agent-based micro-policy simulation model assessing public R&D policy effect when R&D and non-R&D performing companies are located within a network. We set out by illustrating the behavioural structure and the computational logic of the proposed model; then, we provide a simulation experiment where the pattern of the total level of R&D activated by a fixed amount of public support is analysed as function of companies' network topology. More specifically, the suggested simulation experiment shows that a larger "hubness" of the network is more likely accompanied with a decreasing median of the aggregated total R&D performance of the system. Since the aggregated firm idiosyncratic R&D (i.e., the part of total R&D independent of spillovers) is slightly increasing, we conclude that positive cross-firm spillover effects - in the presence of a given amount of support - have a sizeable impact within less centralized networks, where fewer hubs emerge. This may question the common wisdom suggesting that larger R&D externality effects should be more likely to arise when few central champions receive a support.
引用
收藏
页数:22
相关论文
共 55 条
[1]   Modelling Research Policy: Ex-Ante Evaluation of Complex Policy Instruments [J].
Ahrweiler, Petra ;
Schilperoord, Michel ;
Pyka, Andreas ;
Gilbert, Nigel .
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 2015, 18 (04)
[2]   The network of scientific collaborations within the European framework programme [J].
Almendral, Juan A. ;
Oliveira, J. G. ;
Lopez, L. ;
Mendes, J. F. F. ;
Sanjuan, Miguel A. F. .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2007, 384 (02) :675-683
[3]  
[Anonymous], DRUID SUMM C
[4]  
[Anonymous], ANAL COMPLEX NETWORK
[5]  
[Anonymous], 2005, Simulation for the Social Scientist
[6]  
Argote L., 1999, ORG LEARNING CREATIN, DOI 10.1111/j.1552-6909.1999.tb01970.x
[7]  
Arrow K., 1962, RATE DIRECTION INVEN, P609
[8]   Emergence of scaling in random networks [J].
Barabási, AL ;
Albert, R .
SCIENCE, 1999, 286 (5439) :509-512
[9]   Network of European Union-funded collaborative research and development projects [J].
Barber, MJ ;
Krueger, A ;
Krueger, T ;
Roediger-Schluga, T .
PHYSICAL REVIEW E, 2006, 73 (03)
[10]   Quantitative growth effects of subsidies in a search theoretic R&D model [J].
Bental, B ;
Peled, D .
JOURNAL OF EVOLUTIONARY ECONOMICS, 2002, 12 (04) :397-423