Environment Systems and Decisions

, Volume 38, Issue 1, pp 88–91 | Cite as

Learning and open data in sustainability transitions: evolutionary implications of the theory of probabilistic functionalism

  • Masaru Yarime


The Theory of Probabilistic Functionalism, as a general theory of how organisms interact with complex environmental systems, provides a useful framework for describing essential processes of sustainability planning groups. Particularly, the mechanism of producing evolutionary stable representations of and judgment about the environment has important implications for sustainability transitions. In comparison with biological evolution, the environment changes much faster in social phenomena, and the selection dynamics working on heterogeneous groups for successful survival would be incomplete. That makes continuous learning by the members of planning groups as well as collective decision-making among them critically important. The policy of open data for assembling, distributing, and utilizing various kinds of data will facilitate vision creation, strategy development, and consensus building with stakeholders for sustainability transitions.


Theory of probabilistic functionalism Sustainability transition Evolutionary process Learning Open data 

The Theory of Probabilistic Functionalism (TPF) of Egon Brunswik is introduced by Scholz as a general theory of perceptual and cognitive complexity management in inextricably coupled organism–environment systems (Scholz 2017). He discusses convincingly how it provides valuable insight into human–environment interactions and how the organism manages to cope with the complexity of the environment. One of his key arguments is that the principles of TPF, which include adaptive functionalism, dualist human–environment system, probabilistic information acquisition and processing, vicarious mediation, representative design, evolutionary stabilization, and interlinkage of perceptors, can function as an integration device for describing essential processes of sustainable planning groups. He emphasizes the relevance and implications of TPF for understanding how we can manage sustainability transitions, a key challenge we currently face at the global scale, by making a parallel correspondence between visual perception and planning groups’ sustainability transitions.

The process of sustainability transitions basically follows five steps of a problem-solving cycle, namely problem definition, problem representation, the construction of options for future development, the evaluation of these options, and the transformation of these systems. In problem definition and goal formation, the planning group defines system boundaries and the layer of the system, that is, the initial focal variable and the future state, which is the terminal focal variable. In case selection and system representation/scenario construction, the group represents the case by a set of variables, or a set of cues, which may be subsystems, aspects, system variables, or impact factors that sufficiently represent the case and its dynamics under the perspective of sustainable development. In projection and construction of scenarios/alternatives, the planning group identifies one or more future settings that would be considered sustainable. In scenario evaluation, the planning group evaluate one or a small set of scenarios for sustainability transitions, for example, by a multi-criteria assessment. Finally, in step of socially robust orientations or planning variants, the planning group develops strategies or socially/socio-technologically robust orientations for the key actors of a system that support a sustainable transition, based on negotiation processes including the trade-offs of values and interests among the stakeholders.

Scholz argues that the principles of TPF with respect to perception might have the same status as Darwin’s evolutionary principles (Scholz 2017). TPF and evolution are both regarded as theories describing basic principles of natural order. Darwin’s principles explain the emergence of biological complexity, independent of species and scale. In a similar manner, it is argued that TPF principles help to understand how organisms and humans perceive and interact with the environment. In particular, evolutionary stabilization is a crucial concept in TFP. The idea, which is operationalized by the signal detection theory, is that as we have different types of errors, such as a failure to detect a signal of interest, and correct responses of identification or rejection, each of these outcomes has certain costs or benefits in a specific situation of perception, judgment, and decision-making. The accuracy of the judgment or behavior needs to adapt to the long-term benefits and costs inherent in different types of errors or correct responses, as otherwise the perceiver would not be able to survive in the context of the current environment. In this way the judgment or behavior that produces the largest benefits or smallest costs will be selected in the end in the process of evolution.

From long time ago ideas were expressed by scholars on significant potentials in obtaining implications from biological evolution to understand social, particularly economic, phenomena. Veblen, for example, regarded economy as an evolutionary system (Veblen 1898), Marshall mentioned that the Mecca of the economist lies in economic biology (Marshall 1890), and Hayek and Schumpeter also discussed the relationships between economics and evolutionary theory. Despite the attention paid by these prominent economists to the importance of evolution, however, the dominant paradigm of economics was basically the idea that the economy is an equilibrium system, essentially a system at rest. The primary inspiration for economists was not biology but physics, in particular the physics of motion and energy (Beinhocker 2006). It was assumed that economy as a system moves from an equilibrium point to another over time, propelled by shocks from technology, politics, and other external factors.

There are some caveats in applying functional explanation, as in the case of the theory of evolution, to societal events and phenomena (Elster 1989). The biological theory of evolution basically rests on two mechanisms. One mechanism is required to generate variety, which is brought about by a steady stream of random changes, or mutations, in the genes. Also another mechanism is necessary to select and retain the few mutations that happen to be beneficial. The organism in which the useful mutations have occurred can be expected to leave more offspring than others, with the genetic mutations to be found in a larger proportion of the next generation. The biological theory of evolution creates a presumption that whatever benefits the organism’s fitness to the environment could also be explained functionally by the contribution to the evolution of the species in the end. In social sciences, on the other hand, the basic building block, the elementary unit of explanation, is the individual behavior guided by some intention. We therefore need to clarify the mechanism about how an intentional behavior at the individual level would produce long-term consequences at the systemic level.

The relevance and salience of the theory of evolution to economics was thoroughly explored by Nelson and Winter in An Evolutionary Theory of Economic Change (Nelson and Winter 1982). The basic principles of variation, selection, and inheritance were applied for explaining the dynamic change in firms and technologies through the market. In their evolutionary model, firms, which are motivated by profit making, are engaged in research and development in search for creating innovation. The most successful firms are likely to drive the less successful ones out of business, and, as a result, these surviving companies expand their market share, diffusing their technologies and services. Firms are modeled as having certain capabilities and decision rules at any given time, and these capabilities and rules are modified over time as a result of deliberate problem-solving efforts. Imitation from competitors, particularly from those who are successful, is also intentionally pursued, so that those characteristics that are considered to be beneficial for survival will be adopted subsequently by others in the population.

What is particularly important in considering the mechanisms of evolution is that any selection process takes place in a changing environment. Changes in the natural environment normally occur relatively slowly, and thus, there would be sufficient time available for gradual adaptation in the long process of biological evolution. The social environment, in contrast, changes much faster, compared with the competitive process in which successful firms expand, while others are forced out. That implies that evolutionary processes in society are primarily driven by two factors, that is, imperfect market selection delivering prizes and penalties in terms of growth and survival across heterogeneous populations, and idiosyncratic, mistaken-ridden learning by persistently heterogeneous organizations, which would be relatively more powerful than the inter-organizational selection dynamics (Dosi and Nelson 2010).

In a complex, rapidly evolving environment, where adaptation would become close to hitting a moving target, organizational learning plays a critical role. The evolutionary approach emphasizes the organization as a processor of knowledge, that is, it is considered as the locus of generation, augmentation, and application of knowledge. Organizational learning in a changing environment requires sophisticated capabilities for securing ready access to accurate information about the state of the environment and facilitating prompt creation of useful knowledge for effective decision-making.

Learning is of critical importance in sustainability transitions as well. In the theory of probabilistic functionalism, the principle of evolutionary stabilization states that the generated visual image, estimate, or judgment has to be good enough for the perceiver to survive in the discourse of evolution (Scholz 2017). Accordingly, planning studies need to contribute to making systems more compatible, viable, and resilient in the discourse of sustainability and the competition of systems. As in the case of firms, however, the environment in which planning groups operate changes much faster than the process of competitive force-out of those with poor planning. That implies that our struggle to move toward sustainability transitions is primarily driven through learning by planning groups, coping with long time ranges and multiple uncertainties of complex, multi-level, highly interacting, coupled human–environment systems.

Here we should note that there are some differences between groups and organizations (Scholz 2011). In organizations, membership is more formal, and there exist at least two echelons, which is characteristic for the control hierarchy, whereas the members of groups have a set of shared values and goals so that they can maintain their overall pattern of activities in more voluntary and informal manners. As groups work in a less hierarchical environment, learning would take place based on more voluntary initiatives by the participants. That might make it more demanding to manage the process of learning by groups, compared with organizations, which would have more direct control over the activities of the members.

A planning group for sustainability transitions needs to deal with the significant level of uncertainty and complexity involved in the environment. The work of a planning group involve assessing critical issues, developing strategies to overcome barriers, and designing future visions, states, and processes for sustainability transitions. All of these tasks require a substantial amount of information concerning various dimensions of the coupled human–environmental systems, which include the current conditions of the natural environment, the latest development of scientific discoveries and technological breakthroughs, and the economic and social activities of the stakeholders. As the mode of decision-making in groups is less hierarchical and less formal, it would be important that the members are engaged in collective learning by sharing the same data among the members so that the process of consensus building could be facilitated.

The policy of open data supports planning groups in designing visions and developing strategies sustainability transitions (Yarime 2017). An increasing amount of data concerning environmental, economic, social, and technological aspects of sustainability is available from those sources that were not accessible in the past, thanks to remarkable progress achieved recently in advanced technologies, including space satellites, drones, and the Internet of Things. Salient and reliable data provide a solid basis for understanding the structure of the issues they are currently facing and developing strategies for the future. By facilitating data access, exchange, and sharing, open data will promote mutual learning and collective decision-making.

At the same time, for sustainability transitions as social phenomena, we also need to consider their political dimensions and their implications. Sustainability transitions are inherently political and require broad understandings of the politics as encompassing, long-term processes of multiple changes in socio-technical systems (Avelino et al. 2016). The politics includes not only formalized democratic processes or geopolitical struggles, but also micro-politics involved in interactions among the stakeholders, which have not been necessarily investigated in detail in the previous studies on sustainability transition. Politics would be inevitably involved in various stages of sustainability transitions, particularly in deciding on what visions to explore in the future and what criteria to apply for evaluating the state of sustainability. Integration of credible and legitimate data incorporating diverse aspects is expected to contribute to facilitating collaboration and coordination among the stakeholders involved. Making the best use of the integrated data will help us develop, experiment, and advance scientifically and socially robust solutions to complex, ill-defined challenges in sustainability transitions.

TPF, as a general theory of how organisms interact with complex environmental systems, provides a useful framework for describing essential processes of sustainability planning groups. Particularly, the mechanism of producing evolutionarily stable representations of and judgment about the environment has important implications for sustainability transitions. In comparison with biological evolution, the environment changes much faster in social phenomena, and the selection dynamics working on heterogeneous groups would be incomplete. That makes continuous learning by the members of planning groups as well as collective decision-making among them critically important. The policy of open data for collecting, distributing, and utilizing various kinds of data will facilitate vision creation, strategy development, and consensus building with relevant stakeholders for sustainability transitions.


  1. Avelino F, Grin J, Pel B, Jhagroe S (2016) The politics of sustainability transitions. J Environ Plan Policy Manage 18(5):557–567CrossRefGoogle Scholar
  2. Beinhocker ED (2006) The origin of wealth: evolution, complexity and the radical remaking of economics. Harvard Business School Press, BostonGoogle Scholar
  3. Dosi G, Nelson RR (2010) Chapter 3—technical change and industrial dynamics as evolutionary processes. In: Bronwyn HH, Nathan R (eds) Handbook of the economics of innovation. Elsevier, AmsterdamGoogle Scholar
  4. Elster J (1989) Nuts and bolts for the social sciences. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  5. Marshall A (1890) Principles of economics. Macmillan, LondonGoogle Scholar
  6. Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Belknap Press of Harvard University Press, CambridgeGoogle Scholar
  7. Scholz RW (2011) Environmental literacy in science and society: from knowledge to decisions. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  8. Scholz RW (2017) Managing complexity: from visual perception to sustainable transitions—contributions of Brunswik’s theory of probabilistic functionalism. Environ Syst Decis 37(4):381–409Google Scholar
  9. Veblen TB (1898) Why is economics not an evolutionary science? Q J Econ 12(3):373–397CrossRefGoogle Scholar
  10. Yarime M (2017) Facilitating data-intensive approaches to innovation for sustainability: opportunities and challenges in building smart cities. Sustain Sci 12(6):881–885CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Energy and EnvironmentCity University of Hong KongKowloonHong Kong
  2. 2.Department of Science, Technology, Engineering and Public PolicyUniversity College LondonLondonUK
  3. 3.Graduate School of Public PolicyUniversity of TokyoTokyoJapan

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