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Social Data Science
Systems Dynamics and Complexity
Agent-Based Modelling of Social Systems
Cuttlefish: Dynamic Network Visualization
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PhD Positions Data-driven Modeling of Social Systems
Research and development (R&D) networks
Research and Development (R&D) networks are a specific instance of collaboration networks, one of our overarching research areas. Here, the nodes of the network represent firms and links represent their R&D collaborations which are usually explicitely announced. Firms can enter or leave the network and links only have a finite life time, during which firms exchange knowledge in order to increase their own knowledge stock.
While most collaborations are bilateral, i.e. only involve two firms that not necessarily have to be connected to the rest of the network, firms can also form consortia, i.e. small clusters in which nodes are fully connected. Most importantly, collaborations come with a cost because firms give up some of their exclusive knowledge and have to invest effort to maintain the collaboration. These costs have to be compensated by benefits from the collaboration, for example joint patents.
This opens several challenging questions about the empirics of R&D networks and their dynamical modeling by means of an agent-based approach. As our publications witness, we address the full range of research questions from strategic network formation to large-scale temporal data analysis. This allows us to address issues such as the impact of cost-benefit relations (for example, severance costs for breaking up or indirect benefits from being part of a network) on the structure of the R&D network. Specifically, what are the conditions for stability (resulting from individual optimalization) and efficiency (resulting aggregated optimalization)?
Regarding the network dynamics, we explore the role of conditions for the mutual acceptance of a collaboration, such as firm's available information about their counterparties. Different from simplistic models, firms strategically decide about link formation and termination. Eventually, on the aggregated level R&D networks follow a characteristic life cycle dynamics, triggered by innovation booms in specific sectors (e.g. biotech, computer hardware) and their subsequent decline after a few years. Our models are able to predict such structural changes and map them with surprising precision to the conditions of the rise and fall of alliances, on the firm level. The following animated video, which we have produced to show the evolution of a real global R&D network, gives a precise idea of what our models are able to analyse, generate and predict. For more details, please check our publications listed below.
Eventually, this research area also contributed to two other areas, i.e. predicting success from dynamical interaction (here, success is measured by the number of patents) and mechanism design to enhance cooperation. In essence, firms in research alliances are not only collaborators, they are also cooperators in the first place. The success of R&D alliances can be only enhanced, but not guaranteed.
Data-driven modeling of collaboration networks: A cross-domain analysis
Tomasello, Mario Vincenzo; Vaccario, Giacomo; Schweitzer, Frank
We analyze large-scale data sets about collaborations from two different domains: economics, specifically 22.000 R&D alliances between 14.500 firms, and science, specifically 300.000 co-authorship relations between 95.000 scientists. Considering the different domains of the data sets, we address two questions: (a) to what extent do the collaboration networks reconstructed from the data share common structural features, and (b) can their structure be reproduced by the same agent-based model. In our data-driven modeling approach we use aggregated network data to calibrate the probabilities at which agents establish collaborations with either newcomers or established agents. The model is then validated by its ability to reproduce network features not used for calibration, including distributions of degrees, path lengths, local clustering coefficients and sizes of disconnected components. Emphasis is put on comparing domains, but also sub-domains (economic sectors, scientific specializations). Interpreting the link probabilities as strategies for link formation, we find that in R&D collaborations newcomers prefer links with established agents, while in co-authorship relations newcomers prefer links with other newcomers. Our results shed new light on the long-standing question about the role of endogenous and exogenous factors (i.e., different information available to the initiator of a collaboration) in network formation.
This paper studies the selection of partners for R&D collaborations of firms both empirically, by analyzing a large data set of R&D alliances over 25 years, and theoretically, by utilizing an agent-based model of alliance formation. We quantify the topological position of a firm in the R&D network by means of the weighted k-core decomposition which assigns a coreness value to each firm. The evolution of these coreness values over time reconstructs the career path of individual firms, where lower coreness indicates a better integration of firms in an evolving R&D network. Using a large patent dataset, we demonstrate that coreness values strongly correlate with the number of patents of a firm. Analyzing coreness differences between firms and their partners, we identify a change in selecting partners: less integrated firms choose partners of similar coreness until they reach their best network position. After that, well integrated firms (with low coreness) choose preferably partners with high coreness, either newcomers or firms from the periphery. We use the agent-based model to test whether this change in behavior needs to be explained by means of strategic considerations, i.e. firms switching their strategy in choosing partners dependent on their network position. We find that the observed behavior can be well reproduced without such strategic considerations, this way challenging the role of strategies in explaining macro patterns of collaborations.
Drawing on a large database of publicly announced R&D alliances, we empirically investigate the evolution of R&D networks and the process of alliance formation in several manufacturing sectors over a 24-year period (1986-2009). Our goal is to empirically evaluate the temporal and sectoral robustness of a large set of network indicators, thus providing a more complete description of R&D networks with respect to the existing literature. We find that most network properties are not only invariant across sectors, but also independent of the scale of aggregation at which they are observed, and we highlight the presence of core-periphery architectures in explaining some properties emphasized in previous empirical studies (e.g. asymmetric degree distributions and small worlds). In addition, we show that many properties of R&D networks are characterized by a rise-and-fall dynamics with a peak in the mid-nineties. We find that such dynamics is driven by mechanisms of accumulative advantage, structural homophily and multiconnectivity. In particular, the change from the "rise" to the "fall" phase is associated to a structural break in the importance of multiconnectivity.
This paper investigates the process of knowledge exchange in inter-firm Research and Development (R&D) alliances by means of an agent-based model. Extant research has pointed out that firms select alliance partners considering both network-related and network-unrelated features (e.g., social capital versus complementary knowledge stocks). In our agent-based model, firms are located in a metric knowledge space. The interaction rules incorporate an exploration phase and a knowledge transfer phase, during which firms search for a new partner and then evaluate whether they can establish an alliance to exchange their knowledge stocks. The model parameters determining the overall system properties are the rate at which alliances form and dissolve and the agents' interaction radius. Next, we define a novel indicator of performance, based on the distance traveled by the firms in the knowledge space. Remarkably, we find that - depending on the alliance formation rate and the interaction radius - firms tend to cluster around one or more attractors in the knowledge space, whose position is an emergent property of the system. And, more importantly, we find that there exists an inverted U-shaped dependence of the network performance on both model parameters.
We develop an agent-based model to reproduce the process of link formation and to understand the effect of knowledge exchange in collaborative inter-firm networks of Research and Development (R&D) alliances. In our model, agents form links based on their previous alliance history and then exchange knowledge with their partners, thus approaching in a knowledge space.
We validate our model against real data using a two-step approach. Through an inter-firm alliance dataset, we estimate the model parameters related to the alliance formation, at the same time reproducing the topology of the resulting collaboration network. Subsequently, using a dataset on firm patents, we estimate the parameters related to the process of knowledge exchange.
The underlying knowledge space that we consider in our study is defined by real patent classes, allowing for a precise quantification of every firm's knowledge position. We find that real R&D alliances have a duration of around two years, and that the subsequent knowledge exchange occurs at an extremely low rate - a firm's position is rather a determinant than a consequence of its R&D alliances. Finally, we propose an indicator of collaboration performance for the whole network and, remarkably, we find that the empirical R&D network extracted from our data does not maximize such an indicator. However, we find that there exist configurations that can be both realistic and optimized with respect to the collaboration performance. Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.
We develop an agent-based model to reproduce the processes of link formation and knowledge exchange in a Research and Development (R&D) inter-organizational network. In our model, agents form links based on their network features, i.e. their belonging to one of the network's circles of influence and their previous alliance history, and then exchange knowledge with their partners, thus modifying their positions in a metric knowledge space. Furthermore, we validate the model against real data using a two-step approach. Through the Thomson Reuters SDC alliance dataset, we estimate the model parameters related to the link formation, thus reproducing the topology of the resulting R&D network. Subsequently, using the NBER data on firm patents, we estimate the parameters related to the knowledge exchange process, thus evaluating the rate at which firms exchange knowledge and the duration of the R&D alliances themselves. The underlying knowledge space that we consider in our real example is defined by IPC patent classes, allowing for a precise quantification of every firm's knowledge position. Our novel data-driven approach allows us to unveil the complex interdependencies between the firms' network embeddedness and their technological positions. Through the validation of our model, we find that real R&D alliances have a duration of around two years, and that the subsequent knowledge exchange occurs at a very low rate. Most of the alliances, indeed, have no consequence on the partners' knowledge positions: this suggests that a firm's position - evaluated through its patents - is rather a determinant than a consequence of its R&D alliances. Finally, we propose an indicator of collaboration performance for the whole network. We find that the real R&D network does not maximize such an indicator. Our study shows that there exist configurations that can be both realistic and optimized with respect to the collaboration performance. Effective policies to obtain an optimized collaboration network - as suggested by our model - would incentivize shorter R&D alliances and higher knowledge exchange rates, for instance including rewards for quick co-patenting by allied firms.
From the perspective of innovation economics evolving institutions, innovating entrepreneurs, technological change, and creative destruction are the driving force of economic growth (Schumpeter, 1942). To mitigate the uncertainty involved in the creation of new processes, products, or business models, innovation exhibits an intrinsic collaborative nature. Innovator networks form through formal and informal collaborations between different agents, including firms, institutions, universities, state agencies, inventors, and other stakeholders of the innovation system. Being embedded in a network enables these agents to coordinate innovative efforts, as well as to pool and jointly create knowledge.
We develop an agent-based model of strategic link formation in Research and Development (R&D) networks. Empirical evidence has shown that the growth of these networks is driven by mechanisms which are both endogenous to the system (that is, depending on existing alliances patterns) and exogenous (that is, driven by an exploratory search for newcomer firms). Extant research to date has not investigated both mechanisms simultaneously in a comparative manner. To overcome this limitation, we develop a general modeling framework to shed light on the relative importance of these two mechanisms. We test our model against a comprehensive dataset, listing cross-country and cross-sectoral R&D alliances from 1984 to 2009. Our results show that by fitting only three macroscopic properties of the network topology, this framework is able to reproduce a number of micro-level measures, including the distributions of degree, local clustering, path length and component size, and the emergence of network clusters. Furthermore, by estimating the link probabilities towards newcomers and established firms from the data, we find that endogenous mechanisms are predominant over the exogenous ones in the network formation, thus quantifying the importance of existing structures in selecting partner firms.
We investigate the efficiency and stability of R&D networks in a model with network-dependent indirect spillovers. We show that the efficient network structure critically depends on the marginal cost of R&D collaborations. When the marginal cost is low, the complete graph is efficient, while high marginal costs imply that the efficient network is asymmetric and has a nested structure. Regarding the stability of network structures, we show the existence of both symmetric and asymmetric equilibria. The efficient network is stable for small industry size and small cost. In contrast, for large industry size, there is a wide region of cost in which the efficient network is not stable. This implies a divergence between efficiency and stability in large industries.
We introduce a new model for the evolution of networks of firms exchanging knowledge in R&D partnerships. Innovation is assumed to result from the recombination of knowledge among firms in an R&D intensive industry. The decision of two firms to establish a new partnerships or to terminate an existing one, is based on their marginal revenues and costs, which in turn depend on the position they occupy in the network. Moreover, the formation of a collaboration has significant external effects on the other firms in the same connected component of the network. We show that this decentralized partner selection process leads to the existence of multiple equilibrium structures. Finally, by means of computer simulations, we study the properties of the emerging equilibrium networks and we show that they reproduce the stylized facts of R&D networks.
We consider a dynamic model of network formation where agents form and sever links based on the centrality of their potential partners.We show that the existence of capacity constrains in the amount of links an agent can maintain introduces a transition from dissortative to assortative networks. This effect can shed light on the distinction between technological and social networks as it gives a simple mechanism explaining how and why this transition occurs.
We develop a new framework for modeling innovation networks which evolve over time. The nodes in the network represent firms, whereas the directed links represent unilateral interactions between the firms. Both nodes and links evolve according to their own dynamics and on different time scales. The model assumes that firms produce knowledge based on the knowledge exchange with other firms, which involves both costs and benefits for the participating firms. In order to increase their knowledge production, firms follow different strategies to create and/or to delete links with other firms. Dependent on the information firms take into account for their decision, we find the emergence of different network structures. We analyze the conditions for the existence of these structures within a mathematical approach and underpin our findings by extensive computer simulations which show the evolution of the networks and their equilibrium state. In the discussion of the results, particular attention is given to the emergence of direct and indirect reciprocity in knowledge exchange, which refers to the emergence of cycles in the network structure. In order to motivate our modeling framework, in the first part of the chapter we give a broad overview of existing literature from economics and physics. This shows that our framework bridges and extends two different lines of research, namely the study of equilibrium networks with simple topologies and the dynamic approach of hypercycle models.
We investigate some of the properties and extensions of a dynamic innovation network model recently introduced in [Koenig et al. Games and Eco. Beh. 75-2 p694-p713 (2012)]. In the model, the set of efficient graphs ranges, depending on the cost for maintaining a link, from the complete graph to the (quasi-) star, varying within a well defined class of graphs. However, the interplay between dynamics on the nodes and topology of the network leads to equilibrium networks which are typically not efficient and are characterized, as observed in empirical studies of R&D networks, by sparseness, presence of clusters and heterogeneity of degree. In this paper, we analyze the relation between the growth rate of the knowledge stock of the agents from R&D collaborations and the properties of the adjacency matrix associated with the network of collaborations. By means of computer simulations we further investigate how the equilibrium network is affected by increasing the evaluation time $ tau$ over which agents evaluate whether to maintain a link or not. We show that only if $ tau$ is long enough, efficient networks can be obtained by the selfish link formation process of agents, otherwise the equilibrium network is inefficient. This work should assist in building a theoretical framework of R&D networks from which policies can be derived that aim at fostering efficient innovation networks.