THE SIMULATION OF INNOVATION NETWORKS FOR POLICY Nigel Gilbert1 , Petra Ahrweiler2 and Andreas Pyka3 1
2
Centre for Research on Simulation in the Social Sciences, University of Surrey, Guildford, UK
[email protected]
Research Center Media and Politics, Institute for Political Science, University of Hamburg, Germany 3
Economics Department, University of Augsburg, Germany
ABSTRACT A multi-agent simulation embodying a theory of innovation networks has been built and used to suggest a number of policy-relevant conclusions. The simulation animates a model of innovation (the successful exploitation of new ideas) and this model is briefly described. Agents in the model representing firms, policy actors, research labs, etc. each have a knowledge base that they use to generate ‘artefacts’ that they hope will be innovations. The success of the artefacts is judged by an oracle that evaluates each artefact using a criterion that is not available to the agents. Agents are able to follow strategies to improve their artefacts either on their own (through incremental improvement or by radical changes), or by seeking partners to contribute additional knowledge. It is shown that different parameter sets for the model yield behaviours that are qualitatively similar to those of innovation networks in biotechnology and mobile communications. Experiments with the model yield generalisations that may hold lessons for innovation policy.
KEYWORDS Innovation, Research and development policy, Social networks, Social simulation.
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INNOVATION NETWORKS
Innovation – the successful development and marketing of new products, processes and ideas – is increasingly recognised as requiring the convergence of many sources of knowledge and skill. Few innovations can be attributed to a single technological field or even to just one firm (Nelson, 1993). Innovation policy has therefore turned to the encouragement of networks and collaborations between organisations as a means of enhancing the rate of innovation. An innovation network may be defined as a set of actors (people or organisations) that interact with the purpose of generating innovations. Some innovation networks are ‘designed’ by the participants with the explicit goal of creating a structure that will enhance the potential for innovation, but many others are created through cooperation agreements or supplier-client relationships without any conscious realisation that the effect is to form a network. Networks can be large, involving tens of actors, or small, with only a few firms. Innovation networks are often formed from a variety of types of actors. For example, in one area that we have been studying, research on mobile telecommunications, the network consists of mobile telecom equipment manufactures, mobile network operators, the government, and a number of universities. In biotechnology, another one of our case studies (Pyka and Saviotti, 2000), networks are typically composed of one ‘large diversified firm’ (such as Glaxo-Wellcome) and a few dedicated biotechnology firms (DBFs). Figure 1 is a web page from a member of such a network.
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Figure 1. A web page of a biotechnology company, advertising its capabilities and its network partners The growth of innovation networks has occurred at the same time as there has been increasing pressure in the business world to innovate. Competition to produce new products and processes has intensified as the profitability of mass production has declined in the western world, in favour of customised and complex products. The latter embody more formalised knowledge to manufacture and market, and often this knowledge is best obtained through collaboration with other organisations having different capabilities from one’s own. Innovation networks are therefore a growing phenomenon in Europe and America, and commonly involve not only firms, but also independent research labs, universities, and government agencies. Innovation networks have also been boosted by the improvements in information technologies that allow much easier communications between organisations.
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UNDERSTANDING INNOVATION
Although recent work in evolutionary economics (e.g. Nelson and Winter, 1982; Nelson, 1987) and science policy (e.g. Gibbons et al., 1994) has examined the role of innovation networks in technical change, the detailed mechanisms of network formation, the structures that are formed, and their consequences for innovation have not been studied in depth. One reason for this is that it has proved difficult to describe the complex dynamics of the evolution of innovation networks using conventional methods (Pyka, 1999; Teitelbaum and Dowlatabai, 2000; Ziman, 2000). Networks are formed of heterogeneous units with different capabilities; they develop in environments that are themselves changing as a result of innovations made elsewhere; and the effectiveness of networks depends crucially on bringing together the knowledge and skills of the actors. In short, networks are complex adaptive systems: they are generally self-organising, adaptive to their environment, have no central control mechanisms, and their current state is dependent of their past history (Holland, 1992). Innovations can be seen as emergent and unpredictable outcomes of the operation of the networks. These characteristics make it hard to apply the usual forms of analysis. In their place, the SEIN project has been modelling the growth of innovation networks using simulation (Gilbert and Troitzsch, 1999). The Self-organising Innovation Networks (SEIN) project is developing a simulation platform for the investigation of the dynamics of technological collaborations using computational experiments. The project has also gathered data on four examples of innovation network in order to test the model it is developing. These four case studies are of biotechnology in France (Pyka and Saviotti, 2000); combined heat and power (CHP) plants in Germany, the Netherlands and the United Kingdom (Weber and Paul, 1999); knowledge intensive business systems (KIBS) in England and the
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Netherlands (Windrum, 2000); and personal and mobile communications in the UK (Vaux, 2000). These case studies have been chosen in part because they represent a wide range of types of network (Ahrweiler, 1999), from the small and relatively informal networks characteristic of KIBS to the government-inspired, formal and institutionalised network (called a ‘Virtual Centre of Excellence’, or VCE) in mobile communications. In CHP and the mobile VCE, political actors play a large role; in the other two areas, this is not so. In order to understand the case studies and the reasons for the differences between them, the project has created an abstract model of innovation networks, embodied as a computer simulation. This paper reports some results from that simulation. The model has been applied to the case studies, thus gaining a further understanding of: • how networks arise, grow and die; and • whether the observed networks can be considered to be different manifestations of similar underlying processes, or whether the networks are fundamentally different in origin and effects in the four areas; • what are the critical factors and constraints that encourage or discourage the formation of networks.
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THE MODEL
The model is a multi-agent system, that is, each of the actors is represented by an agent or ‘object’ in the program (Gilbert and Terna, 2000). The agents are designed to have the attributes of ‘intelligent agents’: autonomy, ability to interact with other agents; reactivity to signals from the environment; and proactivity to engage in goal-directed behaviour (Wooldridge and Jennings, 1995). To model the knowledge that the actors possess, each is a given a kene, a structured collection of technological, political, social and economic capabilities (Gilbert, 1997). A kene is used to represent the knowledge base of an actor. Kenes change as actors acquire knowledge from other actors, and as they refine their knowledge through research and development. Kenes are made up of capabilities, and each actor has one or more abilities for each capability. For example, a biotechnology firm might have the capability to synthesis a particular pharmaceutical ingredient using a specific manufacturing operation (its ability). An actor’s ability varies along a scale of expertise, which increases when the ability is used and decreases when neglected. When the actor’s expertise in a particular ability declines to zero, the corresponding capability is regarded as having been forgotten and is no longer available to the actor, Actors use the knowledge represented in their kenes to produce artefacts, which depending on the setting might be a new design, a new drug, an invention for which a patent application could be made, or a new discovery publishable in the scientific literature. These artefacts are merely potential innovations. Only a small proportion of them become innovations, that is, successful new products and processes. The selection of which artefacts are innovations is modelled by the innovation oracle. The oracle rejects unsuccessful artefacts and rewards actors that produce successful innovations. For the purpose of the model, the oracle maintains a multi-dimensional ‘innovation landscape’ onto which all possible artefacts can be mapped. The ‘height’ of the landscape at the point where the proposed innovation is located is used to determine whether the artefact is an innovation, and if so, the amount of reward that flows back to the innovating actor. The form of the landscape is complex and unknown to the actors and they cannot anticipate with certainty how successful their innovations will prove to be. Successful innovations deform the landscape so that the reward for a second artefact identical to the first innovation is much reduced (this models the fact that in most fields the first mover will patent or copyright their innovation, with the result that subsequent identical artefacts from other producers receive little or no reward). The rewards for some potential innovations close to the artefact are increased to reflect the tendency for innovations to pave the way for other similar innovations in the market. As a result of these effects, the landscape co-evolves with the actors’ kenes, and this is one way in which the model reproduces the complexity of the real world. An actor’s kene can develop as a result of three factors. First, an actor can use its resources to engage in ‘incremental’ or normal research and development (R&D). This improves the actor’s ability relating to a specific capability using its experience with previous artefacts. Second, the actor can elect to engage in ‘radical’ research in which entirely new artefacts are created from new combinations of the agent’s capabilities. This corresponds to a firm deciding that it needs to branch out into a new area of expertise. Thirdly, an actor can learn from another actor with whom it is 3
collaborating in a partnership. Partners share their knowledge when a partnership is formed, thereafter producing their own artefacts from their own kenes. Actors are always available to join partnerships and whether they in fact do so depends on their strategy for developing their kenes, and also on whether they are able to find partners that are sufficiently attractive. All actors display an ‘advertisement’ consisting of a list of the capabilities that they possess (but not the details of the actual abilities that they have, since these will be confidential and in many cases, are not easily made explicit). For example, a biotechnology start-up might advertise that it is capable of performing some aspect of genomics, thus making itself attractive to other firms without that expertise, and possibly to venture capitalists or large firms seeking partners to develop new markets. Actors use the advertisements to judge whether the capabilities of a potential partner are sufficiently tempting to warrant forming a partnership. They use either a progressive or a conservative strategy in evaluating potential partners. For the progressive strategy, the actor looks for partners possessing capabilities that it does not have. The conservative strategy values partners according to the number of capabilities that they and the actor have in common. Partnerships are considered to be relatively short-term relationships focused on the development of particular products. They are typically binary relationships, although some actors may enter into a number of collaborations with different partners. In some fields, more permanent and more densely connected networks are also found. These networks always include a number of actors, bound into a collaboration which is more enduring than a partnership and which often has a distinct identity (ranging from an informal name to a legally based company, as with the Virtual centre of Excellence in Mobile and Personal Communications, which is a non-profit limited company with its members as the shareholders). In the model, agents are able to convert a set of partnerships into a network provided that all the members have previously been partners. The advantage is that the network pools its members’ capabilities and has a lower cost of collaboration than would have been the case if the members had only binary partner relationships. The actors are driven by a need to maximise their rewards. Set against the rewards provided by the oracle for successful innovations are the costs of performing R&D and of collaboration and networking. Actors that fail to accumulate sufficient rewards to keep themselves in funds are declared bankrupt and ‘die’. On the other hand, if the population of actors is successful in earning rewards, start-ups enter the field, copying the kenes of the most successful actors and thus increasing the level of competition. Figure 2 portrays the model schematically.
Start-up Actor with kene Actor with kene Actor with kene
Network
New Capabilities and Abilities Search for a partner Missing capabilities
Artefact
Research and development
Reward Decide whether to seek a partner
Innovation oracle
Figure 2. Schematic of the model 4
To summarise, the actors engage in individual learning, both through an incremental process and through radically altering their potential innovations. They also learn from their partners. At the population level, therefore, the actors collectively gradually migrate through the innovation landscape exploring areas of locally greater reward, while at the same time changing the landscape through their explorations. Less successful actors earn little or no reward and are eventually culled, while start-ups begin with a kene that is similar to the most successful actor. Thus the population as whole evolves through a process of learning, selection and the reproduction of success.
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TESTING THE MODEL
There are a number of parameters in the model that can be adjusted to study various types of network environments. These include the form of the innovation landscape, the costs of networking and carrying out R&D, and the strategies that actors use to choose network partners. As an illustration of the output from the model, Figure 3 plots the innovations that are generated for an artificial situation when the landscape consists of a single peak in multi-dimensional space, and when the landscape remains unchanged despite the incidence of innovations. This does not mirror any real setting, but it does allow one to see fairly clearly how the population of actors gradually ‘learn’ the location of the peak and modify their kenes to produce innovations that are at or near it. Figure 4 presents the same plot, but with the landscape being modified as innovations are accepted by the oracle, thus generating a moving target for the actors. Figure 5 gives another view of the way in which the actors learn, showing the average and maximum rewards earned by the actors over time.
Figure 3. Plot of innovations when the landscape has a single peak and is not deformed by innovations
Figure 4. Plot of innovations when the landscape has a single peak and is deformed by innovations
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Figure 5. Plot of rewards for best innovation (upper) and mean reward for successful innovations (lower) by simulation time step
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MODELLING BIOTECHNOLOGY AND THE VCE
The model described in the previous section is an abstract one that does not attempt to mirror the characteristics of any particular innovation network. It was tested by seeing whether by adjusting its parameters, its behaviour could be altered to reproduce in a qualitative sense the characteristics of networks found empirically in two sectors: biotechnology and mobile communications. If successful, this would help to validate the model and also indicate which attributes led to the very different types of innovation network found in the two areas (the model has also been fitted to a third domain, knowledge-intensive business services, but the results will not be reported here). As mentioned above, the Virtual Centre of Excellence in Personal and Mobile Communications (the ‘VCE’) is a network set up with the encouragement and assistance of the UK Government that involves most of the mobile phone operators, manufacturers and researchers involved in the UK market. It is a single large network that is now in its fourth year and aims to continue with the same members for at least two more years. It grew quickly to its present size, and has a stable membership with few very small firms involved. In contrast, biotechnology tends to have many bilateral collaborations rather than large networks. Small start-ups are common, many of which either fail or are absorbed by other firms, so that there is a high rate of entry and exit, and a very unequal distribution of firm sizes with many small and a few very large firms. To produce simulated networks that had the characteristics of either the VCE or biotechnology sectors, six parameters were adjusted. These are shown in Table 1. For the simulation of the VCE using the parameters in the second column, the model exhibited a low rate of entry and exit and firms rapidly joined networks, as shown in Figure 6. In most runs of the model using these parameters, a small number of networks developed, but it was rare for there to be only one. We conclude that the evolution of a single network in the absence of a powerful integrating actor such as the government is unlikely but possible. A plot of the average and largest rewards earned by the firms at each time step (see Figure 7) shows that there was a gradual increase in success as the networks distributed knowledge about rewarding capabilities between their members. However, the average reward then declined because the networks were ‘locked in’ to particular artefacts whose rewards decreased as the innovation landscape was deformed.
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Table 1: Parameters that were changed in order to model the VCE and Biotechnology cases Parameter Actors at start
VCE 30, each with the same initial capital
Attractiveness required for potential partners in order to justify partnership Number of partners to consider in the search for a sufficiently attractive partner Number of successive artefacts that can be submitted to the oracle and receive no reward before the network is dissolved for lack of success Capital value that must be achieved by the most successful firm to attract a start-up to enter the area
Low
Biotechnology 10 ‘large’ firms and 30 small ones. The large firms had ten times the starting capital of the small ones High
High
Low
30
15
Medium (10,000 units)
High (25,000 units)
Figures 6. The VCE case: number of actors (green); number in partnerships (blue) and number in networks (red).
Figure 7. The VCE case: average (blue) and largest (red) rewards at each time step.
The behaviour of the actors in the simulation using the ‘biotechnology’ parameter values (see Table 1) is very different. As Figure 8 shows, the early period is characterised by considerable shake out of the smaller firms, but the success of the best actors leads to a compensating influx of small firms. There is a very unequal size distribution of firms, with the few large firms created at the beginning of the simulation joined by a large number of small firms (see Figure 9). Although many actors collaborate in bilateral partnerships and networks, as is shown by the red and blue lines in Figure 8, those involved are always less than half of the total.
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Figure 8. The Biotech case: number of firms (green); number of firms exiting (black); number of firms in partnerships (blue) and number in networks (red).
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Figure 9: The Biotech case: distribution of capital between firms at time step 300.
POLICY IMPLICATIONS
Development of the model has led to some interesting speculations about factors that could encourage or constrain the rate of innovation. One is that although learning from other actors in a network can be effective in generating new knowledge and thus new innovations, it can be disruptive to have too much new knowledge flowing in, since the new knowledge can displace useful capabilities. Secondly, the simulation has emphasised the importance of the entry of new actors into the system. Start-ups have often been encouraged because they promote employment. But the model demonstrates another value of start-ups. By cloning ideas from the most successful existing actors, they have a significant effect on the mean level of success of the population of actors. Thirdly, it has been observed from the model that small stable networks gradually lose their effectiveness as the environment (the innovation landscape) changes faster than they do. Thus, it may be that although encouraging the formation of innovation networks will increase the pace of innovation, these networks should not be allowed to continue indefinitely. Finally, experiments indicate that, at the level of the population as a whole, the strategies that individual firms use to select partners are not very important. If they use an unsuccessful strategy (no innovations result from the network), the network and possibly the actors themselves die to be replaced by others, while the more successful networks continue and come to dominate the field.
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CONCLUSIONS
A model of innovation networks has been constructed. The model itself represents a theory of how innovation networks arise and grow. The theory argues that participants have bundles of knowledge that they are able to improve either on their own, or through collaboration. The model shows that collaboration can help actors develop more rewarding innovations, but that this needs to be tempered with a capacity for continuing research and development. Relying on knowledge from partners alone can lead to stasis. The model also shows that the innovative performance of the population as a whole is very dependent on the best actors; indifferent performers leave the population and thus help to improve the average performance of the population as a whole.
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It was also shown that the model can be applied to simulate the characteristics of two very different industrial sectors and the types of innovation network found in these. This suggests that that the underlying mechanisms that yield innovation networks are likely to be shared across sectors rather than each sector having fundamentally different mechanisms. Finally, the experiments with the VCE and biotechnology cases suggest that the critical factors that affect whether an sector is characterised by short-term bilateral partnerships or long-term, large networks include the search behaviour of the firms: whether they look for partners that are similar or different from themselves and how much effort they put into finding suitable partners. These are factors that can be influenced by government in the pursuit of its R&D policy objectives. Future research will examine the effect of varying other parameters in the model in order to understand better the sensitivity of its behaviour to a variety of policy-relevant changes.
ACKNOWLEDGEMENTS The SEIN project is supported by the European Commission’s Framework 4 programme, contract SOEI-CT-98-1107. We gratefully acknowledge the assistance and advice of other members of the project, and of Glen E. Ropella and Sven Thomesen of the Swarm Corporation. Working papers from the project can be found at the project’s web-site: http://www.uni-bielefeld.de/iwt/sein/. This is an extended version of a paper originally presented to the Eurosim conference, Delft, June 2001.
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