Our hypotheses will often be simply wrong. That is obvious and universally acknowledged. The article wobbles between criticising specific theory neo-classical approaches, rational expectations , specific simplifying assumptions that are often applied to a range of theories e. If Mr Syll knows exactly what the problem is with econometrics he has not made it clear. He prefers a blunderbuss: shoot at everything; some target will get hit. Mr Syll is also very exercised by the realisation that social systems are evolving and unlikely to be stable over long periods — a fact of life of which all sentient economists are aware.
No sensible econometrician imagines he or she is discovering immutable truths. They are taking existing theories and asking could the theory, allowing for a penumbra of uncertainty caused by omitted variables, really have generated the data we observe? Is it an adequate model? This question can only be posed and answered probabilistically. Introspection, intuition, imagination, blind prejudice can all generate theories about society. Ultimately, if we are to make progress such theories have to confront data. We can never conclusively prove a theory.
Despite Popper, we can not conclusively disprove a theory either, though we can dismiss it with a high degree of probability. One important means of dismissal is the intelligent application of econometrics. The fact that the tool can be misused and often has been misused does not change the fact that it is the most important tool we have for advancing knowledge. Econometrics does not have to assume linearity, exogeneity of explanatory variables, normal distribution of errors or any of the other assumptions of convenience that may be adopted. Sometimes those assumptions are pernicious, sometimes harmless in context.
More to the point all can be tested in any particular case. It is hard to avoid the suspicion that Mr Syll simply does not like confronting numerical data and would rather live in a world of undisciplined speculation. What Lars Syll points out, we all know. There is nothing new in it. The real issue is to find out the alternative methodology. Perhaps, the contemporary mathematics and statistics are not rich enough to analyze the real phenomena with limited data or if there are any, they are unknown to the economists.
We are aware of the problems of covariance among the explanatory variables, but we do not have a dependable method to deal with the problem. We are aware of misspecification problems, but we do not have enough information to specify the model correctly. We acknowledge nonlinearity and perhaps multiple solutions multiple optima and nonconvexity but we do not have a foolproof method to handle it.
We understand the issues of risk and uncertainty, but we do not have methods to deal with them appropriately. We understand the problem of non-quantifiability, but we do not have appropriate methods to deal with it. Then, the answer is not to criticise and destroy. The answer is to develop new methods, learn from other disciplines, invent, and do a constructive work.
It is much more difficult than simply criticising. I know well what it means, and others too know it. Those of us in the economics community who are impolite enough to dare to question the preferred methods and models applied in mainstream economics and econometrics are as a rule met with disapproval. Nothing is perfect … The assumptions are reasonable. The biases will cancel. We can model the biases. Now we use more sophisticated techniques.
What would you do? You have to do the best you can with the data. You have to make assumptions in order to make progress. You have to give the models the benefit of the doubt. Lars is absolutely right in pointing out that those who dare question the status quo are met with disapproval, even intimidation and contempt. Well done, Lars—please keep it up. I loved your comment! Richard E. Planck, M. Firstly, the use of formal and logical modeling of our macroeconomics system has taken us far, unlike the negative claim given above. It has taken us so far that it has strangely managed to show by the use of quite a simple numerical method, that an increment in the taxation of personal incomes has a positive effect on national prosperity, when taken at large, but that when the same sum is collected from the taxation of land values, the benefit is roughly 3 times as big.
These facts were first derived in my recent book, which I suggest be taken a bit more seriously. I can send you an e-copy, so you can check the arithmetic chesterdh hotmail. Secondly any model cannot simply be wrong since it does represent something or some concept that by its nature deals with our subject, and it does have some limited resemblance to it. Thirdly, probability has no place here.
We do not model a situation with the belief that it has some probability of being accurate. That is the nature of taking this imaginative methodology. I support Lars fully. The paper has actually an eye opener in it before the econometricians. Successful economic thoughts have least bothered about the metrics so far. Those who have influenced the transformations in civilizations have seen the past with full devotion, taken insights from there but not got driven by then.
Historicity gives an easy pathway towards estimating forward but limits it to the lame contexts of the past and attempts to impose on the future. Thanks Professor R P Banerjee rpbanerjee10 hotmail. I found Mr. I think he could have gone much further. Plutonometrics without compassionate wisdom and vast knowledge of the situation is rational justification of rape, pillage, plunder, normative psychopathology, systemic cultural corruption and the anti-ethical Piracy Paradigm that perpetuates it all.
Clearly, as Syll and others see and understand, any model that fails to closely approximate the actuality of human culture and its activities, despite being officially accepted as a technically valid and useful tool, serves as diversionary, subversive camouflage, obfuscating the nature and purpose of The Plutonomy Game. I am confident that model predicts the outcome of every game, matching the current financial state of the world with exact accuracy. The only way to make the model more accurate would be to include religious factions, governments, armaments, armies, terrorists, police, multi-national corporations and every kind of psychopathic player available.
It includes new equations and formulas enabling integration of quantitative and qualitative data. It needs corrections and expansion, but you may find it helpful. I agree with several of the assertions of Lars Syll, but it is true that his arguments are a bit of a mixture. Analysis based on econometrics seems to assume s the following, regarding each point: a Impacts are linear or tractable as linear e. Assumptions a and b are strictly econometric, and are at its core.
Assumptions c and d instead are more related to the use of econometric outcomes, both by theoreticians and practitioners. It is not Econometrics, as the Syll rightly points out. Econometrics per se is not able to solve this question, besides the usual calculus of correlations, as causality arguments are embedded in theory, and it is theory that have to decide whether there is or not relationship among two variables.
Theoretically we must allow that the influence of one variable over another may change in its intensity; e. In this point, linearity may be too restrictive. But this is a rather technical issue, that perhaps more sophisticated econometrics will solve in the future: indeed, some non linear specifications are quite usual nowadays.
Non linear forms are preferred when a constant elasticity is the desired outcome. It is a deep if solvable epistemological problem to decide whether a process features randomness. Rolling a dice or throwing a coin — two noteworthy examples of randomness — are not random at all; are mechanical processes, just too complex to be predictable in analytical terms. It seems that there has been a confusion between the random tables o generators used for sampling and the very idea of random variable.
There remain c and d ; as told before, these are economic questions, and therefore the answer to them must arise from Economics, not from Econometrics. The clue question is the following: Is Economics able or entitled to find empirical relationships that are transferable through time and context? The lab in the future or in other place is able to replicate the same processes and outcomes.
Syll clearly asserts that this kind of universal statements are precluded for Economics. But if this question — to be or not to be a Science — is rather definitional, it is not that important. We all agree that economic analysis is able to deliver useful assertions for the problems it has decided to face at least for the problems that are really economic, i. For example, when Dani Rodrik argues that the Washington Consensus meant wrong policies for Latin America in the Nineties, this assertion is based both on empirical and theoretical grounds, and can be rationalized as such.
Dong, C. Robust planning of energy management systems with environmental and constraint-conservative considerations under multiple uncertainties. Duncan, R. Technological change in the arid zone of New South Wales. Ettoumi, F. Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution. Energy 28, Frei, C. Dynamic formulation of a top-down and bottom-up merging energy policy model. Energy Policy 31, Grubb, M.
Energy Environ. Gustafsson, S. Mathematical modelling of district-heating and electricity loads. Energy 46, Hedenus, F, Azar, C. Ssrn Elibrary. Heinzelman, W. Energy-efficient communication protocol for wireless microsensor networks. Presented at the System Sciences, Proceedings of the 33rd Annual Hawaii International Conference on, p. Hodge, B. A prototype agent-based modeling approach for energy system analysis, in: 18th European Symposium on Computer Aided Process Engineering.
Elsevier, pp. Jebaraj, S. An optimal energy allocation model using fuzzy linear programming for energy planning in India for the year Energy Technol. Policy 5, - Jiang Chang, and Shu-Yun Jia, Modeling and application of wind-solar energy hybrid power generation system based on multi-agent technology 3, Kanagawa, M.
Analysis of the energy access improvement and its socio-economic impacts in rural areas of developing countries. Ecological Economics, 62, Kanniappan, P. Optimization model for energy generation from agricultural residue. Energy Res. Kaya, T. Expert Syst Appl 38, Ko, F-K. Long-term CO 2 emissions reduction target and scenarios of power sector in Taiwan. Energy Polic y 38, Li, S. Deterministic fuzzy time series model for forecasting enrollments.
Li, Y. F, Li, Y. P, Huang, G. Energy and environmental systems planning under uncertainty-An inexact fuzzy-stochastic programming approach. Energy 87, Lin, Q. Interval-fuzzy stochastic optimization for regional energy systems planning and greenhouse-gas emission management under uncertainty-a case study for the Province of Ontario, Canada. Change , Liu, P Energy systems planning for the Province of Saskatchewan.
Liu, Y, A dynamic two-stage energy systems planning model for Saskatchewan, The University of Regina, Canada. Logenthiran, T.
Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Power Syst. Ma, T. Modeling technological change in energy systems - From optimization to agent-based modeling. Energy 34, Messner, S. Endogenized technological learning in an energy systems model. Mirzaesmaeeli, H. A multi-period optimization model for energy planning with carbon dioxide emission consideration.
University of Waterloo, Canada.
Murphy, F. Neuhoff, K. Energy J. Volume Modeling long-term dynamics of electricity markets. Pandey, R. Energy policy modelling: agenda for developing countries. Energy Policy, 30, Pereira, A. Energy 36, Reich-Weiser, C. Presented at the Electronics and the Environment, ISEE Ren, H. Life Syst. Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects. Sadeghi, M.
Energy supply planning in Iran by using fuzzy linear programming approach regarding uncertainties of investment costs. Safari, S. Particle swarm optimization based fuzzy logic controller for autonomous green power energy system with hydrogen storage. Sirikum, J. Power generation expansion planning with emission control: a nonlinear model and a GA-based heuristic approach. Salas, P. Schafer, A. P Thery, R. Energy planning: A multi-level and multicriteria decision making structure proposal. Technological change at the regional level: the role of location, firm structure, and strategy.
Urban, F, Benders, R. Modelling energy systems for developing countries. Energy Policy, 35, 6, Van Vliet, O. Synergies in the Asian energy system: Climate change, energy security, energy access and air pollution. Energy Econ. Wei, Y. Progress in energy complex system modelling and analysis.
Energy Issues 25, Zhu, Y. An interval full-infinite programming approach for energy systems planning under multiple uncertainties. Received 13 October Revised 27 September All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License. Services on Demand Article. English pdf Article in xml format Article references How to cite this article Automatic translation.
Access statistics. Cited by Google Similars in Google. Introduction Over the past three decades, energy planning and management EPM has played an essential role in long-term social, environmental, and economic policy making of countries. Approaches of characterizing energy models The energy models existing in the literature can be categorized via different ways; however, these categorizations are related to each other. Model type Descriptive or prognostic models depict or describe how things actually work or might work, and answer the question, 'What is this?
Purpose Energy models are usually designed to address specific questions and hence, are only suitable for the purpose they were developed. Exploring: Scenario analysis is utilized for exploring the future. Back casting: This approach is used to determine the conditions of a desired future and to define steps to attain a desired vision of the future. Modelling paradigm The difference between top-down and bottom-up models is related to the technological and sectoral aggregation.
The underlying methodology In the following part, an overview of commonly used methodologies in developing energy models will be presented. Resolution technique At the level of concrete models, a further distinction can be made considering the resolution tools utilized in the models. Geographical coverage Energy models may analyse different levels of geographical and spatial areas. Sectoral coverage The economy can be divided into certain sectors. The time horizon The time frame defined in energy models are usually categorized as short term day, month, till 5 years , medium term from 5 to 15 years , and long term beyond 15 years Grubb et al.
Data type Aggregated and disaggregated data are two extremes for required data of energy models. Endogenization degree A model with high endogenization degree is one that its parameters are incorporated within the model equation so as to minimize the number of exogenous parameters. Addressed side Energy models are usually designed to deal with demand side issues such as demand forecasting, or supply side e.
Trends in energy models characteristics A first step in developing an appropriate model is to make decision about its characteristics which were previously introduced according to the defined problem. Analytical approach Top-down and bottom up approaches are two extreme modelling paradigms. The second approach namely; a modular approach is developed in order to compute equilibrium price and quantities by iterative interactions between various modules Problem formulation Energy systems as an integral part of socio-economic systems of societies have several cross-disciplinary interactions with the economy, society, and environment.
Problem environment According to the literature, both certain and uncertain environments have been assumed for EPM problems. As local companies develop a competitive edge, they have tended to move out of the region, generating a threat of deindustrialization. This threat has continuously to be countered by these industrial districts Dei Ottati, ; Sforzi, Such observations indicate the occurrence of unintended consequences: bifurcations are generated when the diffusion dynamics of the market becomes more important than the local production dynamics.
In some cases and at certain stages of the innovation process, local stabilization in a geographic area may prove beneficial, for example, because of the increased puzzle-solving capacity in a niche. However, at a subsequent stage this advantage may turn into a disadvantage because the innovations may become increasingly locked into the local conditions. As various subdynamics compete and interact, the expectation is that a more complex dynamics will emerge.
Therefore, the institutional perspective on a system of innovation has to be complemented with a functional analysis. A focus on the geographical perspective of national systems without this awareness of changing boundaries can be counterproductive Bathelt, What seemed institutionally rigid under a previous regime e. In a follow-up study, Nowotny et al. The possibilities for novelty and change are limited more in terms of our current capacity to reconstruct expectations than in terms of historical constraints. How does one allocate the capacities for puzzle-solving and innovation across the system when the system boundaries become so fluid?
The authors of the Mode-2 thesis answer as follows:. Instead, choices emerge in the course of a project because of many different factors, scientific, economic, political and even cultural. These choices then suggest further choices in a dynamic and interactive process, opening the way for strategies of variation upon whose further development ultimately the selection through success will decide.
Nowotny et al. The perspective, consequently, is changed from interdisciplinary—that is, based on careful translations among different discourses—to transdisciplinary —that is, based on an external management perspective. The global perspective provides us with more choices than were realized hitherto. This global perspective emerges from reflexive communications. While Lundvall had focused on interaction and argued that communications can stabilize the local innovation environment for agents, the authors of the Mode-2 thesis argued that reflexive communications can provide us with a global perspective on the relevant environments.
This global perspective enables us to assess the historically grown opportunities from the perspective of hindsight. In other words, the global perspective adds a dynamic that is different from the historical one which follows the time axis. While the latter focuses on the opportunities and constraints of a given unit e. The robustness of a construct would no longer depend on its historical generation, but on the present level of support that can be mobilized in terms of expectations from the various subsystems of society e.
In other words, the complexity is reduced from the perspective of choosing the specific window of learning on the complex dynamics. The claim of this encompassing perspective among a variety of perspectives has appealed to policy makers Hessels and Van Lente, From the Triple Helix perspective, this reduction of uncertainty by choosing a single perspective—on the basis of the assumption of a prevailing process of de-differentiation—means unnecessarily sacrificing explanatory power.
What needs to be explained, are the interactions among the different perspectives.
Indeed, this might reify an analytical perspective. The political subdynamic, however, remains part of the system which is supposedly to be steered from this perspective. Because of the nested dynamics, the political subsystem can function in some instances as the steering variable, and at a next moment of time encounter itself as a dependent variable; for example, in the case of unintended consequences.
In the complex dynamics of communication, unintended consequences of specifically coded communications can be expected to prevail because each communication is part of a distribution of communications necessarily containing uncertainty. Since the complex dynamics consists of subdynamics which select upon each other, the system can also be resilient against steering, and in some phases more than in other Van den Daele et al. Thus, the question of a empirical study modeling and simulation of systems of innovation remains crucial.
Because of the subsidarity principle in the EU which specifies that the Union should leave to national governments what does not require harmonization at the transnational levels, science policy initiatives had successfully been defended by national scientific elites against European intervention during the s Mulkay, The European Committee had circumvented this blockage by focusing on innovation policies—as different from science and technology policies—in the successive Framework Programs. In addition to demanding that two or more nationalities be represented in the bids for these programs, collaborations between universities, industries, and public research centers were required before one could qualify for obtaining a grant.
In the USA, however, these systems are both federal and national, and therefore traditionally controlled by scientific elites at the national level Brockman, In the European configuration, a trans-national system of quality control would also require the construction of a trans-disciplinary frame of reference. The bibliometric framework of citation analysis cannot provide this frame of reference, because this would reproduce traditional differences among disciplines and national cultures in Europe. For example, the more internationally oriented national countries such as in Scandinavia, the UK, Ireland, and the Netherlands could then dominate the framework.
However, this underestimates the problems involved in proceeding from discursive reconstruction to deliberate action. The latter presumes informed choices among and decisions about ranges of options that reproduce the complexity under study according to a complex dynamics that is different from its politically controlled subdynamic. This other perspective is possible because a network contains a dynamic both at the level of the nodes and at the level of the links. While agency can be considered as a source or recipient of communication—and can be expected to be reflexive, for example, in terms of learning and entertaining preferences—an agent has a contingent position at a node in the network Burt, The links of a communication system, however, operate differently from the nodes in the network.
Links can be replaced for functional reasons and densities in the networks can thus migrate as an unintended consequence. Concepts like reflexivity and knowledge have different meanings from one layer of the network to another and these different layers can be made the subject of other discourses. For example, agents entertain preferences, but the structure of the network of communications provides some agents with more access than others. In addition to actions which generate the variations, the dynamics of communications, that is, at the level of the links, are transformative Bhaskar, ; , at p.
These changes are endogenous to the network because they can be the result of non-linear interactions among previously stabilized aggregates of actions.
Recursions and interactions add non-linear terms to the results of micro-actions. Luhmann was the first to propose that communication among agents be considered as a system of reference different from agency. An interaction can be attributed as an action to an actor, while it can be expected to function as a communication within a communication system Maturana, ; Leydesdorff, However, the reflection by a social system operates differently from reflection at the level of individual consciousness.
Human language enables us not only to provide meaning to the communication of information, but also to communicate meaning on top of the first-order dynamics of information.
Energy models: Methods and characteristics
Words may have different meanings in other contexts. The evolutionary dynamics of social communication can add another layer of complexity to the first-order dynamics of information exchanges among agents. The self-organization of communication into various functionally different coordination mechanisms on top of the institutional organization of society in a national system enables the social system to process more complexity than in an organizationally controlled mode.
However, under this condition one can expect to lose increasingly the notion of accountable centers of coordination; central coordination is replaced with a number of more abstract and interacting coordination mechanisms. The interacting sub systems of communication can become increasingly differentiated in terms of their potential functions for the self-organization of the social system.
This communication regime reshapes the existing communication structures as in a cultural evolution. In summary, the communicative layer provides society with a set of selection environments for historical institutions. In the case of communication systems, however, selections operate probabilistically, that is, with uncertainty. Translations among the differently coded communication may reduce the uncertainty.
Thus, these selection mechanisms can only be specified as hypotheses. The specification of these expectations, however, guides the observations in terms of specifiable uncertainties e. Furthermore, these communication dynamics of the social system are complex because the codes of the communication have been differentiated historically Strydom, Communications develop along the functionally different axes, but they can additionally be translated into each other by using the different codes at interfaces reflexively.
Thus, systems of translation are generated. In university-industry-government relations three types of communications are interfaced. Let me now turn to my thesis that the utilization of the degrees of freedom between institutions and functions among the three subsystems interacting in a Triple Helix enables us to understand these processes of innovation.
The systems-of-innovation approach defined innovation systems in terms of institutional units of analysis. The Triple Helix approach combines these two perspectives as different subdynamics of the systems under study. However, this model enables us to include the dynamics of the market as a third perspective.
As noted, the perspective of neo-classical economics is micro-founded in the natural preferences of agents. Thus, one can assume that innovation systems are driven by various subdynamics to varying extents. Within this complex dynamic, the two mechanisms specified above—user-producer interactions and reflexive communications—can be considered as complementary to the micro-foundation of neo-classical economics. First, each agent or aggregate of agencies is positioned differently in terms of preferences and other attributes. Second, the agents interact, for example in economic exchange relations.
This generates the network perspective. Third, the arrangements of positions nodes and relations links can be expected to contain information because not all network positions are held equally and links are selectively generated and maintained. The expected information content of the distributions can be recognized by relevant agents at local nodes. These recognitions provide meaning to the events and these meanings can also be communicated. The recognition thus generates knowledge bases both at the addresses of the agents, in their organizations, and at the level of society.
Knowledge can also be processed as discursive knowledge in the network of exchange relations. Figure 6 summarizes this configuration. With this visualization I intend to make my argument epistemologically consistent by relating the above reflections to the underlying dimensions of the Triple Helix model. The three analytically independent dimensions of an innovation system were first distinguished in Figure 3 above as 1 the geography which organizes the positions of agents and their aggregates; 2 the economy which organizes their exchange relations; and 3 the knowledge content which emerges with reference to either of these dimensions Archer, Given these specifications, we were able to add the relevant interaction terms.
The second-order interaction among these interactions then provides us with the hypothesis of the development of a knowledge base endogenous to the system under study. Exchange relations. Figure 6 specifies the knowledge base as an interaction between discursive and tacit knowledge. Along the three axes, the three micro-operations are represented. Each agency agents, institutions, nations has a position in the network and can be considered as naturally gifted with a set of preferences. This assumption accords with the micro-foundation of neo-classical economics.
The network dynamics are first micro-founded in terms of natural preferences. Learning in relations, however, can change both the agents and their institutions by embedding them in specific e. National systems of innovation can then be considered as specific forms of organization which reduce transaction costs Williamson, , For example, the Scandinavian environment might generate an institutional framework which changes transaction costs to such an extent that a second independent dynamics can be sustained in addition to the market mechanism.
However, one can rotate the figure and change the order of the axes without any loss of explanatory power: three micro-mechanisms are involved: one based on positions, a second based on the possibility of exchange, and a third based on the possibility for agent-based and discursive learning. When the learning is grounded in agency, social psychological mechanisms and categories can be used for the analysis. However, when the learning is carried by distributions in networks, the socio-psychological categories provide us only with metaphors, but the operations have to be specified differently because networks can be expected to contain a dynamics different from agency.
For example, agency tends to integrate conflicting perspectives by making trade-offs. Networks allow for other solutions, such as differentiation when different control mechanisms are available. For example, normative and analytical considerations can be entertained, distinguished, and traded-off at different positions in the network. The second-order interaction between learning in individuals and networks generate configurational knowledge as a next-order regime of expectations.
Selection is structural. Three helices are sufficiently complex to understand the social reproduction of the dynamics of innovation Leydesdorff, ; cf. What is observable can be specified as relative equilibria at interfaces between two selection mechanisms operating upon each other. When repeated over time, each co-variation can be developed into a co - evolution, and a next-order, that is, more complex, system can be generated in a process of mutual shaping among the interactions. I have argued that the Triple Helix can be elaborated into a neo-evolutionary model which enables us to recombine sociological notions of meaning processing, economic theorizing about exchange relations, and insights from science and technology studies regarding the organization and control of knowledge production.
The further codification of meaning in scientific knowledge production can add value to the economic exchange relations Foray, ; Frenken, The model can serve as a heuristics. Its abstract and analytical character enables us to explain current transitions towards a knowledge-based economy as a new regime of operations. The Triple Helix model thus substantiates and operationalizes the general notion of a knowledge-based economy as a self-organizing system Krugman, The differentiation in terms of selection mechanisms can be both horizontal and vertical.
Vertically the fluxes of communications are constrained by the institutional arrangements that are shaped in terms of stabilizations of previous communications. Horizontally, the coordination mechanisms can be of a different nature because they can be expected to use different codes. For example, market transactions are different from scientific communications. Market transactions can also be cross-tabulated with organizational hierarchies Williamson, ; Lundvall, While the control mechanisms at interfaces can be considered as functional for the differentiation among communications, the hierarchy in the organization may help to reduce the problem of coordination between functions to a multi-level problem within the institutional dimension.
In summary, the functional perspective is different from the institutional one. Functional communications evolve; institutional relations function as retention mechanisms which respond to functional incentives. The specification of functions in the socio-economic analysis requires reflexivity. All reflections can again be made the subject of communication. Thus, one can study a Triple Helix at different levels and from different perspectives.
For example, one can study university-industry-government relations from a neo- institutional perspective e. Different interpretations of the Triple Helix model can be at odds with each other and nevertheless inform the model. Each metaphor stabilizes a geometrical representation of an otherwise more complex dynamics.
Competing hypotheses derived from different versions of the Triple Helix can be explored through formal modeling and appreciated through institutional analysis. The case studies inform the modeling efforts about contingencies and boundary conditions, while simulation models enable us to relate the various perspectives. From this perspective, innovation can be considered as the reflexive recombination at an interface, such as between a technological option and a market perspective. Specification of the different contexts, however, requires theorizing.
For the purpose of innovation, the perspectives have to be combined, for example, in terms of a plan. The three strands of the Triple Helix are treated as formally equivalent in the model, but they are substantially very different. The selection mechanisms are expected to operate asymmetrically. The one strand university is institutionally less powerful than the other two strands. Furthermore, the other two strands government and industry are increasingly and indirectly co-opting the university in a variety of ways, even if one disregards the direct influence of the so-called military industrial complex.
However, the university has specific strengths: first, it is salient in providing the other two systems with a continuous influx of new discursive knowledge e. From this perspective, the university can be considered as the main carrier of the knowledge-based innovation system Godin and Gingras, Knowledge-based fluxes continuously upset and reform the dynamic equilibria sought by the two other strands of the political economy.
The Triple Helix model is sufficiently complex to encompass the different perspectives of participant observers e. What is the contribution of this model in terms of providing heuristics to empirical research? First, the neo-institutional model of arrangements among different stakeholders can be used in case study analysis. Given the new mode of knowledge production, case studies can be enriched by addressing the relevance of the three major dimensions of the model.
This does not mean to disclaim the legitimacy of studying, for example, bi-lateral academic-industry relations or government-university policies, but one can expect more interesting results by observing the interactions among the three subdynamics. Secondly, the model can be informed by the increasing understanding of complex dynamics and simulation studies from evolutionary economics e. Thirdly, the Triple Helix model adds to the meta-biological models of evolutionary economics the sociological notion of meaning being exchanged among the institutional agents Habermas, ; Leydesdorff, ; Luhmann, [a].
Finally, on the normative side of developing options for innovation policies, the Triple Helix model provides us with an incentive to search for mismatches between the institutional dimensions in the arrangements and the social functions carried by these arrangements. The frictions between the two layers knowledge-based expectations and institutional interests , and among the three domains economy, science, and policy provide a wealth of opportunities for puzzle solving and innovation. The evolutionary regimes are expected to remain in transition because they are shaped along historical trajectories.
A knowledge-based regime continuously upsets the political economy and the market equilibria as different subdynamics. Conflicts of interest can be deconstructed and reconstructed, first analytically and then perhaps also in practice in the search for solutions to problems of economic productivity, wealth retention, and knowledge growth.
The rich semantics of partially conflicting models reinforces a focus on solving puzzles among differently codified communications reflexively. The lock-ins and the bifurcations are systemic, that is, largely beyond control; further developments are based on the self-organization of the interactions among the subdynamics. The subdynamics can also be considered as different sources of variance which disturb and select from one another Resonances among selections can shape trajectories in co-evolutions, and the latter may recursively drive the system into new regimes.
This neo-evolutionary framework assumes that the processes of both integration and differentiation remain under reconstruction. From the perspective of the information sciences, the above discussion of innovation theory and theories of technological change needs to be complemented with a further specification about the operationalization and the measurement. Can a measurement theory for the communication of meaning and knowledge in a Triple Helix model also be specified? How does the communication of knowledge differ from the communication of information and meaning, and how can these differences be operationalized?
How do the communication of information, meaning, and knowledge as layers in communication systems relate and potentially operate upon one another? How does this vertical differentiation in the codification relate to the horizontal differentiation among the three or more coordination mechanisms in a Triple Helix model? The idea that human beings not only provide meaning to events, but are able to communicate meaning in addition to the communication of information, emerged gradually during the 20 th century with the development of sociology as a discipline.
According to Weber e. Unlike information, meaning cannot be transferred over a cable, but it can be communicated in interactions among reflexive agents. According to Luhmann , sociologists should focus on the dynamics of meaning in communication Luhmann, Habermas , at pp. Luhmann, b, at p. The approach which considers communications not as attribute to organizations and agency, but organizations and agency as constructed in and by interhuman communications, finds its philosophical origins in the Cartesian Mediations, which Husserl wrote in From this perspective, the other in the act of doubting is defined as God.
God transcends the contingency of the cogito , and therefore one can expect this Other to be eternal. Husserl proposed to consider the cogitatum no longer as a personal God, but as the intentional substance among human beings which provides the cogito with an horizon of meanings.
We—as cogitantes —are uncertain about what things mean, and the communication of this uncertainty generates an intersubjectivity which transcends our individual subjectivities. Although meanings are structured at the supra-individual level, these structures are no longer identified with a personal God. On the contrary, meaning can be constructed, enriched, and reproduced among human beings by using language. By using language one is able to relate meanings to one another.
However, within language the world is resurrected as an architecture in which the words can be provided with meaning at the supra-individual level. However, this meaning is not provided by the words or their concatenations in sentences or networks of co-occurrences. Language organizes the concepts by providing specific meaning to the words at specific instances e. The instantiations refer to what could have been differently constructed and understood. In other words, the cogitata are not specific; they remain uncertain. However, Husserl conceded that he had no instruments beyond the transcendental apperception of this domain and therefore he had to refrain from empirical investigations:.
Husserl, , at p. In addition to these methodological advances, some theoretical steps in sociology were crucial. First, Parsons , at p. Ego relates to Alter not only in terms of observable relations, but also in terms of expectations. Ego expects Alter to entertain expectations like those Ego finds in her own mind. The two systems Ego and Alter expect each other to operate in terms of expectations.
While Ego and Alter are defined at the level of individual consciousness, Luhmann , generalized this model as the model of communication between and among meaning-processing systems Vanderstraeten, From this perspective, the Triple Helix model can also be considered as representing a triple contingency among three communication systems Strydom, ; Leydesdorff, For example, money enables us to make economic transactions without having to discuss the price.
Crucial is that meaning can be communicated among human beings, and that the coordination of this communication can become self-organizing—that is, beyond the control of the communicating agents—under the condition of modernity, that is, under the pressure of the functional differentiation of the codes of communication in the various coordination mechanisms. The differentiation in the codification e. However, Luhmann , at p. One can distinguish layers and dimensions at different moments of time, for example, by using subscripts and superscripts as indices.
Structure in the data provides meaning. Structure can be analyzed in observed data by using multivariate statistics, for example, factor analysis. The eigenvectors, in other words, provide us with a second-order dynamics in terms of which changes at the level of relations among vectors—that is, the first-order dynamics in terms of observable data—can be provided with meaning.
Note that meaning can be provided both by an analyst and by a participant-observer within the system under study. In both cases, the relational data is positioned in a vector space with a topology very different from the relational space in which the network is constructed. For example, the vector space is based on continuous coordinates, while graph-theoretical network analysis is based on discrete events. The different topology of the vector space enables us to formalize the concepts of meaning and meaning-processing.
This constructed space can be considered as a cogitatum. It remains a construct that cannot be observed directly. The function of this constructed space is to enable us to communicate intersubjectively the meaning of what one is able to observe in the first contingency of a relational space. Social structure is thus created as a cultural order of expectations.
Changes in the relational space communicate Shannon-type information. Meaning is communicated within a vector space. A difference in the data can make a difference in terms of the organization of the data, and thus be meaningful. In other words, the organization of the data provides the Shannon-type information with meaning. However, not all uncertainty is meaningful, and thus a selection is involved. Selection is structural, but operates as a cogitatum.
Neither the selection environments as structures nor their dynamic functions should be reified from this perspective; they remain constructs and orders of expectations. The function of specifying these selection environments is to enable theoretical discourse to enrich the development by providing it with relevant distinctions in the knowledge base. This operationalization of the communication of meaning in a space with different characteristics provides us with a next step in the full specification of the Triple Helix model.
Meaning-providing subsystems can be considered as carried by the eigenvectors of the networks, that is, as densities in the structures of communications. In the static model the eigenvectors can be spanned orthogonally. Dynamically, they represent selective structures.
The observable variation at the first-order level provides the co-variation among these selective structures. For example, in Figure 1 above, patents were positioned as observable events in a vector space spanned by the three dimensions of the Triple Helix. When three or more dimensions can operate upon one another, mutual information or co-variation in more than two dimensions can also be computed. A negative expected information value of this measure would reduce uncertainty and can therefore be considered as configurational information McGill, Configurational information provides us with an indicator of the interaction among different and potentially orthogonal dimensions.
Note that this reduction of uncertainty at the level of a configuration among functions is analytically different from reduction of uncertainty provided by the factor analysis. The factor analysis reduces the data by capturing the common variances among the variables. This first-order reduction of uncertainty can be considered as a structure in the data to which one can ascribe a semantic meaning for example, by designating the factors. The reduction of uncertainty because of the configuration among the main dimensions eigenvectors of the data matrix distinguishes in a next-order process among meanings which make a difference, and thus indicates the extent to which knowledge as codified meaning can be expected to operate within a system.
Figure 7 : A layered process of codification of information by meaning, and codification of meaning in terms of a knowledge base. Figure 7 summarizes the model. This model includes both horizontal and vertical differentiation. The horizontal differentiation among latent coordination mechanisms was considered by Luhmann as functional differentiation and is operationalized as the orthogonal dimensions which result from the factor analysis. Vertically, the model distinguishes between interactions at the bottom level, potential self-organization of the communications in different configurations at the top level, and structuration by organization at the level in between.
In summary, the evolving networks of relations among agents can be considered as the retention mechanisms of flows of communication through these networks. The flows are structured by functionally different codes of communication Luhmann, [a] , and enabled and constrained by structures in the data at each moment of time Giddens, In a network of political communications, power and legitimacy provide the communications with differently codified meanings.
The functions of flows of communication develop evolutionarily in terms of the eigenvectors of the networks, while the networks of relations develop historically in terms of aggregates of actions. The functions can be operationalized as the latent dimensions eigenvectors of the networks of relations among the agents. However, the eigenvectors develop in a vector space with a topology and dynamics different from those of the relational space among the agents.
For example, relations develop within a space, whereas the vector space can develop as a space, for example, by adding new dimensions. The relations and the nodes are positioned in a vector space Burt, ; positions reflect the meanings of relations events in the various dimensions. Reflexive agents are not only embedded in the relational space which they span, but able to provide meaning to the positions and relations. This can be considered first-order reflexivity. When this reflexivity is made the subject of theoretical reflections, next-order reflexivity is added as an overlay to this dually-layered system of relations and positions.
The increased availability of this more abstract since codified type of communication changes the systems in which it emerges and on which it rests as another selection environment. The processes of reflexive change in such systems are enhanced by adding a knowledge base to the communication.
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However, it tends to remain beyond the control of agents interacting in terms of relations that is, generating uncertainty and positions that is, providing meaning to uncertainty. The knowledge-base of an economy can be considered as this evolving configuration among the functions of coordination mechanisms. Using the Triple Helix model of university-industry-government relations, the carrying functions of a knowledge-based economy were specified above as 1 economic wealth generation, 2 knowledge-based novelty production, and 3 normative e.
Over time, the knowledge base thus generated—indicated as the reduction of uncertainty contained in a configuration—can be stabilized, meta-stabilized, or globalized. Configurations can thus be distinguished in terms of the extent to which a synergy is self-organized among the main subdynamics of a knowledge-based economy.
Note that with the opposite sign, configurations may also frustrate the further development of a knowledge base at the systems level by generating more uncertainty than can be absorbed by the relevant subsystems in their current configuration. This empirical research program remains necessarily a piecemeal enterprise e.
The information sciences are crucially positioned in a configuration at a crossroad among the other relevant sciences such as economics, policy analysis, and innovation studies because of their emphasis on operationalization and measurement. I am grateful to the comments of Diana Lucio-Arias, Andrea Scharnhorst, Wilfred Dolfsma, and a number of anonymous referees on previous drafts of this paper. The paper is partially based on a rewrite of various chapters of Leydesdorff Abramowitz, M.
Measuring Performance of Knowledge-based Economy. In Employment and Growth in the Knowledge-based Economy pp. Paris: OECD. Abramson, N. Information Theory and Coding. New York, etc. Adorno, T. Positivismusstreit in der deutschen Soziologie. Frankfurt am Main: Luchterhand. Allen, P. Andersen, E. Evolutionary Economics: Post-Schumpeterian Contributions. London: Pinter. Aoki, M. Towards a Comparative Institutional Analysis. Archer, M. Realist social theory: the morphogenetic approach. Arrow, K.
The economic implications of learning by doing. Review of Economic Studies, 29, Arthur, W. Increasing Returns and Path Dependence in the Economy. Ann Arbor: University of Michigan Press. Bar-Hillel, Y. An Examination of Information Theory. Philosophy of Science, 22, Barras, R.
2. Terms, analysis, conception of economy
Interactive Innovation in financial and business services: The vanguard of the service revolution. Research Policy, 19, Bateson, G. Steps to an Ecology of Mind. New York: Ballantine. Bathelt, H. Growth regimes in spatial perspective 1: innovation, institutions and social systems. Progress in Human Geography, 27 6 , Beccatini, G. Beck, U. Bell, D. The measurement of knowledge and technology. Moore Eds. Concepts and Measurements pp. The Coming of the Post-Industrial Society.
New York: Basic Books. Berger, P. The social construction of reality: a treatise in the sociology of knowledge. Garden City: Doubleday. Bhaskar, R. The Possibility of Naturalism: a philosophical critique of the human sciences. Brighton: Harvester Press.
Archer, R. Bhaskar, A. Collier, T. Norrie Eds. Biggiero, L. Italian industrial districts: a Triple Helix pattern of problem solving. Industry and Higher Eductation, 12 4 , Self-organizing processes in building entrepreneurial networks: a theoretical and empirical investigation. Human Systems Management, 20 3 , Bowker, G. Memory practices in the sciences : MIT Press. Braczyk, H. Regional Innovation Systems. Braverman, H.
Labor and Monopoly Capital. The Degradation of Work in the Twentieth Century. Brockman, J. The Third Culture. Brooks, D. Evolution as Entropy. Brusoni, S. Knowledge Specialization and the Boundaries of the Firm: Why do firms know more than they make? Administrative Science Quarterly, 46, Burt, R.
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