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Challenges of complexity economics

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By Joachim H. Spangenberg and Lia Polotzek

In recent WEA Commentaries, the issue of complexity theory and its implications for economics have rightfully gained some prominence. However, while the authors picked up some relevant points, the issue deserves a more comprehensive treatment in new economics, beyond mobilising some arguments to bolster ongoing debates. It should be recognised instead that complexity requires a different way of thinking, and of asking questions in economics. Only then the specific tools used in complexity research, unconventional as they are from a standard economics point of view, come into play. Thus we will briefly describe what we see as core elements of complexity, the corresponding world view, and the tools used.


Complexity economics is a genuine theoretical approach based on applying complexity analysis to the economic system; it requires a world view different from the one of neoclassical economics, moving from reductionist linear thinking to non-linear approaches of conceptualising the economy. In system science parlance, a ‘system’ is any set of things within a common frame (the system boundary) that is ruled by a given set of interactions (the system rules). Applying the terminology to the whole of the outside world, three systems have to be distinguished (Sayer 2000; Spash 2012): The ‘real-world system’ or ‘the reality’ is the object we would like to know more about. However, this system is not accessible to direct human observation since our perception is limited by the senses and instruments we have and interpreted by our brain.

The result is a ‘mental model’, an imagination of reality, a simplified system which provides our ontology. The ontology shapes expectation and questions asked, is the basis of the interpretation of experiences and observations, and shapes the recommendations derived from them. However, it is usually neither reflected nor made explicit, often rather being an unconscious model of the world and its functioning the analyst holds. Ontologies, like all mental models, are best described in qualitative narratives or storylines. The third system, computer models, are the tools used to quantify a selected set of the expectations raised by the mental models. They are limited by the system margins and the necessarily relatively simple descriptions of a limited number of interactions within the system. Which elements are taken into account (i.e. realised in the computer model), and which interactions are considered and thus programmed, depend on both the ontology and the limitations imposed by the modelling technology chosen. Surprisingly, most public trust lies in these most simplified models.

While the system boundaries can be defined according to the research question analysed (choosing the subsystem of interest), this is not the case for the system rules which are defining the functioning of a system and its subsystems. The number of rules needed to describe the system functioning is a good measure of the respective system’s complexity – the more rules are given, the more system behaviour is constrained and less complex. On the basis of Allen (2001), we can define five distinct rules, which, if they all apply, signal maximum determination. The five system rules are, simply expressed:

  1. It is possible to distinguish between ‘the system’ and ‘its environment’. Defining the border line is crucial as what the system can describe is only what is inside; it is a condition for the very existence of a system. When economists regret that their predictions did not correctly predict real-world developments, explaining that with unforeseen ‘external factors’, they essentially indicate that they have drawn the border line in the wrong place, excluding factors decisive for the functioning of the system.
  2. All system components can be recognised and distinguished, which means it is possible to describe and at best understand their interaction.
  3. The active system elements are all identical, or at least the range of their behaviour is normally distributed around the average. In an economic system, for instance, consumers and producers are key active system elements. Microeconomics tries to understand their interaction by analysing the interplay between ‘representative agents’– one consumer representing all consumers and one producer representing all producers. To be able to do so, one must assume that all consumers and all producers are identical regarding their behaviour in the situation analysed. In a biological system, the range of behaviour of individuals of the same population tends to be centred around an average (if two dominant patterns exist, they can be considered as the core of behaviourally different subpopulations, which are system elements).
  4. The individual behaviour of the system elements can be described by average interaction parameters which characterise the system behaviour. This implies that producers, consumers and others always follow the same set of behavioural rules and norms (with some stochastic variation); they are extremely stubborn, do not learn or change their behaviour towards others, at least not as groups (this is not a statement about individual behaviour and learning, and similarities with known professions are purely incidental). The rationality of the selfish human in standard economics embodies these characteristics. The result is a deterministic development, with at most a random variation around the predicted outcome.
  5. The system develops towards a stationary equilibrium, which permits defining fixed relations of system variables. If this is the case, the future is perfectly predictable as the development trajectory of the system is defined and unchangeable. This is an abstraction, a mechanistic contract: machines behave like that but no natural, biological or social system does.

Using the five rules, we can distinguish the levels of complexity between different complex adaptive systems. Geo-physical systems like the climate system fulfilling rules 1 to 4 can evolve and adapt, which makes transitions towards different attractor basins possible when external conditions change, a phenomenon we also know as crossing tipping points. Biological systems only match rules 1 to 3. They have a higher degree of complexity due to the individual behaviour of agents which can deviate from the standard behaviour of a representative agent and its fuzzy borders, making transitions towards different attractor basins even easier. Again one dimension more complex are anthropogenic systems (societies, economies, etc.) restricted only by rule 1 and 2, as here the agents are capable of anticipation. Modification of behaviour not randomly but based on expectations can avoid structural changes, but – if as so often expectations are wrong – can also result in accelerated and intensified changes. Unlike for biological models, taking this trait into account is a necessary condition for suitable economic models (alternative mental models, imaginaries – computer models so far fail to deal with this level of complexity). Thus when Maria Alejandra Madi discusses complexity theory in Commentaries 8(4), she is right defining it not as tool driven, but a genuine theoretical approach, but when equating the complexity of natural and anthropogenic systems, she underestimates the systemic differences.

World views

The complexity theory approach is part of a distinct world view. The philosophical literature on the concept of worldview dates back to Immanuel Kant, who coined the term “Weltanschauung” in 1790. In the literature, the elements most frequently discussed as constituents of a worldview are ontology, epistemology, axiology and anthropology (Hedlund-de Witt 2012). Ontology is a section of philosophy dealing with questions concerning the nature of being, and in particular questions regarding how and under what circumstances entities exist or may be said to exist and how such entities may be grouped, related within a hierarchy and subdivided according to similarities and differences. Epistemology is the branch of philosophy dealing with the theory of knowledge. It studies the nature of knowledge, justification and the rationality of belief, describing the kinds of knowledge we can have about an entity identified by the ontology (hence the distinction of three levels of models is already part of our epistemology). Axiology is another branch of philosophy, encompassing a range of approaches to understanding how, why, and to what degree humans should or do value objects (entities), whether the object is physical (a person, a thing) or abstract (an idea, an action), or anything else. According to Hedlund-de Witt (2012), it should include a societal vision. It also determines the ethics pursued and thus should be made explicit when developing proposals for action (the discount rate built into current economic models determines the value of future development, making it an implicit ethics causing a lack of transparency). Philosophical anthropology describes the conditio humana, the essentials of human existence, and the nature of human beings, the latter typically used in the context of ambiguous subjects such as moral concerns and human reflections on the meaning of life.

The neoclassical economics world view describes a world consisting only of monetary flows, with the economy the meta system. The ecological economics ontology which we endorse considers the environment as the meta system in which society is embedded, and the economy is a subsystem of society. Together they form a dynamic panarchy (Gunderson, Holling 2001). All three are complex evolving systems of different complexity, and tend to follow some variant of the Holling cycle of resilience (Holling 2001). In this cycle a phase of expansion, growth and development is followed by one of stabilisation, a metastable state with still dynamic changes, but fixed underlying structures and key relations. It is followed by a phase of disruption, usually a rapid process after passing a tipping point which can start as slow degradation accelerating or happen without previously observable indications. Then reorganisation happens, making use of resources left from the previous cycle but developing a new system. Phases of apparent stability should not be misinterpreted as equilibria: they are rather dissipative patterns far-from equilibrium, with their basic patterns maintained by the permanent throughput of matter and energy (Prigogine, Stengers 1984). The slow-to-no growth situation of most affluent economies can be understood as the metastable interlude between expansion and disruption.

The epistemology used in neoclassical economics is a positivist one, based on the assumption that the world can be fully understood and measured. As opposed to that, the one we use is rooted in the assumptions of critical rationalism. The world is a concrete reality, a complex system characterised by prevailing and unavoidable risk, uncertainty and ignorance. We can perceive reality only indirectly through senses and instruments, which influence our perception, often unconsciously (as critical realism postulates and environmental sociology shows). Our ontology influences the interpretation of observations with a tendency to realign them as long as possible. Models are recognised as delivering incomplete information which needs to be understood in the context of the mental models and ontologies behind them, and be critically reflected. While complexity economics is accepting diverse value systems, the axiology of neoclassical economics is dominated by “economic rationality”, considered an anthropogenic constant which – together with the methodological individualism considering each individual as independent from social influences – is also shaping its anthropology. As opposed to that, complexity economics accepts human beings in their ambivalence as social beings, their behaviour influenced by both egoistic instincts and genuine social practices, shaped by their respective social, institutional and infrastructure context (Spangenberg, Lorek 2019). While according to the insights of sociology, psychology and political science this is more realistic than the standard economic assumptions, it makes predictions almost impossible as there is not one binding logic all individuals must follow at all times.


Greg Daneke in Commentaries 9(2) rightfully describes complexity economics as using specific, unconventional tools such as “a variety of computational tools (nonlinear math, neural nets, cellular automata, adaptive algorithms, etc.) to simulate the co-evolutionary interaction of heterogeneous agents (exhibiting cooperative, reciprocal, and even altruistic behaviours) and their institutions”. To this list focussed on new models and algorithms qualitative methods, text and discourse analysis, empirical methods, polls and questionnaires should be added. Complexity economics is methodologically diverse; models do not play a dominant role as in standard economics but are rather support tools for more complexity bearing narratives.

Thus complexity economics indeed uses different tools than standard economics, and for good reasons. Analysing the available tools from a complexity perspective makes it crystal clear that the tools of economics are undercomplex and will not be able to deliver results adequately describing economic developments (see also Ciarli, Savona 2019). Equilibrium models follow rules 1 to 5 and system dynamic models rules 1 to 4; both are deterministic and have problems dealing with uncertainty and ignorance (stochastic variation as in fuzzy models is not uncertainty). As relative equilibria are considered to be just an interim phase of the Holling cycle, equilibrium models are only justifiable – if at all – for analyses of short term developments. However, in standard economics and in the Integrated Assessment Models (IAM) used in climate science, they are used for the opposite. Agent-based modelling uses identical agents (but usually defines more than two groups of agents) to analyse the interaction mechanisms of societies and adheres to rules 1 to 3. No model matches the complexity of reality (and most mental models); the best available option appears to be a combination of agent based models for social and economic processes, embedded in a system dynamics environment model.

Generally speaking, in order for computer models to be adequate (scientifically rigorous and socially robust), though, the mental model –already a simplification of reality – must first capture the major behavioural traits of ‘reality’, reflect and integrate them as the basis for deriving strategies effective when applied in a real-world context. Only then can the required technical tools be chosen or developed, attempting to enable them to express the main characteristics of the mental model, many of them qualitative in kind, and quantitative functions derived complementing and illustrating the qualitative mental models. As the mental model, expressed in scenario narratives or story lines, can accommodate qualitative aspects in a way no computer model can, the mental model narrative is the matrix in which diverse and complementary computer models can be embedded, illustrating and quantifying specific aspects of the scenario (Alcamo 2001). As both mental models and even more so computer models are simpler than the reality they describe, we should be aware how the simplifications that are inherent to the model (and that indeed is, to a certain degree, its purpose) impact the recommendations derived. In particular, when ‘the reality’ makes itself felt, confronting our expectations with unexpected experiences in a way that cannot be overlooked, the prevailing construction of the two derived systems must be considered falsified and due to change. Unfortunately, this basic principle is not always adhered to in standard economics.


Alcamo, J. (2001). Scenarios as tools for international environmental assessments. EEA Expert Corner Report Prospects and Scenarios No. 5. Expert Corner Reports. EEA European Environment Agency. Luxembourg, Office for the Official Publications of the European Communities: 31.

Allen, P. M. (2001). The Dynamics of Knowledge and Ignorance: Learning the New Systems Science. Integrative Systems Approaches to Natural and Social Dynamics. H. M. W. Matthies, J. Kriz,. Berlin, Heidelberg, New York, Springer: 3-30.

Ciarli, T., Savona, M. 2019. Modelling the Evolution of Economic Structure and Climate Change: A Review. Ecological Economics 158: 51-64.

Daneke, G. 2019. Why Economics is Still Not a Science of Adaptive Systems. WEA Commentaries 9(2).

Gunderson, L.H., Holling, C.S. (eds.) 2001. Panarchy: understanding transformations in systems of humans and nature. Washington D.C., USA, Island Press.

Hedlund-de Witt, A. (2012). “Exploring worldviews and their relationships to sustainable lifestyles: Towards a new conceptual and methodological approach.” Ecological Economics 84: 74-83.

Holling, C.S. 2001. Understanding the Complexity of Economic, Ecological, and Social systems. Ecosystems 2001(4): 390-405.

Madi, M. A. 2018. Complexity in Economics. WEA Commentaries 8(4).

Prigogine, I., Stengers, I. 1984. Order out of Chaos: Man’s New Dialogue with Nature. Toronto-New York-London-Sydney, Bantam Books.

Sayer, A. (2000). Realism and Social Science. London, UK, Sage Publications Ltd.

Spangenberg, J. H. 2015. Sustainability and the Challenge of Complex Systems. J. C. Enders, M. Remig (eds.), Theories of Sustainable Development. Routledge, Abingdon, UK: 89-111.

Spangenberg, J.H. 2016. The world we see shapes the world we create. How the underlying worldviews lead to different recommendations from environmental and ecological economics – the Green Economy example. Int. J. Sustainable Development.

Spangenberg, J.H., Lorek, S. 2019. Sufficiency and consumer behaviour: From theory to policy. Energy Policy 129: 1070-1079.

Spangenberg, J.H., Polotzek, L. 2019. Like blending chalk and cheese – the impact of standard economics in IPCC scenarios. Real-World Economics Review 87: 196-211.

Spash, C.L. 2012. New foundations for ecological economics. Ecological Economics 77: 36-47.

From: pp.8-11 of WEA Commentaries 10(1), February 2020


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11 responses

  • David Harold Chester says:

    Making Macroeconomics a Much More Exact Science

    Today macroeconomics is treated inexactly within the humanities, because at a first look it appears to be a very complex and easily confused matter. But this does not give it fair justice, because we should be trying to find an approach to the topic and examine it in a better way that avoids these problems of complexity and confusion. Suppose we ask ourselves the question: “how many different KINDS of financial (business) transaction occur within our society?” Then the simple and direct answer shows that that only a limited number of them are possible or necessary.

    Although our sociological system comprises of many millions of participants, to properly answer this question we should be ready to consider the averages of the various kinds of activities (no matter who performs them), and simultaneously to idealize these activities so that they fall into a number of commonly shared ones. This employs some general terms for expressing the various types of these transactions, into what becomes a relatively small number of operations. Here, each activity is found to apply between a particular pair of agents—each one having individual properties. Then to cover the whole sociological system of a country, the author finds that it requires only 19 kinds of exchanges of the goods, services, access rights, taxes, credit, investment, valuable legal documents, etc., verses the mutual opposing flows of money. Also these flows need to pass between only 6 different types of representative agents.

    The analysis that led to this initially unexpected result was prepared by the author and it may be found in his working paper (on the internet) as SSRN 2865571 “Einstein’s Criterion Applied to Logical Macroeconomics Modeling”. In this model these 19 double flows of money verses goods, etc., are shown to pass between the 6 kinds of role-playing entities. Of course, there are a number of different configurations that are possible for this type of simplification, but if one tries to eliminate all the unnecessary complications and sticks to the more basic activities, then these particular quantities and flows provide the most concise result, which is presentable in a comprehensive and seamless manner, and one that is suitable for further analysis of the whole system.

    Surprisingly, past representation of our sociological system by this kind of an interpretation model has neither been properly derived nor presented before. Previously, other partial versions have been modeled (using up to 4 agents, as by Professor Hudson), but they are inexact due to their being over-simplified. Alternatively, in the case of econometrics, the representations are far too complicated and almost impossible for students to follow. These two reasons of over-simplification and of over-complexity are why this non-scientific confusion is created by many economists and explains their failure to obtain a good understanding about how the whole system works.

    The model being described here in this paper is unique, in being the first to include, along with some additional aspects, all the 3 factors of production, in Adam Smith’s “Wealth of Nations” book of 1776. These factors are Land, Labor and Capital, along with their returns of Ground-Rent, Wages and Interest/Dividends, respectively. All of them are all included in the model, as a diagram in the paper.

    (Economics’ historians will recall, as originally explained by Adam Smith and David Ricardo, that there are prescribed independent functions of the land-owners and the capitalists. The land-owners speculate in the land-values and rent it to tenants, whilst the capitalists are actually the owners/managers of the durable capital goods used in industry. These items may be hired out for use. Regrettably, for political reasons, these 2 different functions were deliberately combined by John Bates Clark and company about 1900, resulting in the later neglect of their different influences on our sociological system– the terms landlord and capitalist becoming virtually synonymous along with the expression for property as real-estate.)

    The diagram of this model is in my paper (noted above). A mention of the related teaching process is also provided in my short working paper SSRN 2600103 “A Mechanical Model for Teaching Macroeconomics”. With this model in its different forms, the various parts and activities of the Big Picture of our sociological system can be properly identified and defined. Subsequently by analysis, the way our sociological system works can then be properly seen, calculated and illustrated.

    This analysis is introduced by the mathematics and logic that was devised by Nobel Laureate Wassiley W. Leontief, when he invented the important “Input-Output” matrix methodology (that he originally applied only to the production sector). This short-hand method of modeling the whole system replaces the above-mentioned block-and-flow diagram. It enables one to really get to grips with what is going-on within our sociological system. Subsequently it will be found that it is the topology of the matrix which actually provides the key to this. The logic and math are not hard and is suitable for high-school students, who have been shown the basic properties of square matrices.

    By this technique it is comparatively easy to introduce a change to a preset sociological system that is theoretically in equilibrium (even though we know that this ideal is never actually attained–it simply being a convenient way to begin the study). This change creates an imbalance and we need to regain equilibrium again. Thus, sudden changes or policy decisions may be simulated and the effects of them determined, which will point the way to what policy is best. In my book about it, (see below) 3 changes associated with taxation are investigated in hand-worked numerical examples. In fact when I first worked it out, the irrefutable logical results were a surprise, even to me!

    Developments of these ideas about making our subject more truly scientific (thereby avoiding the past pseudo-science being taught at universities), may be found in my recent book: “Consequential Macroeconomics—Rationalizing About How Our Social System Works”. Please write to me at for a free e-copy of this 310 page book and for additional information.

  • Gerald Holtham says:

    Macroeconomics deals with value added, not gross production and ignores raw materials and waste. It is therefore unable to address environmental consequences of and constraints on economic activity. Even events like the oil-price shocks of the past 50 years could not be handled because energy was missing from the production function. The macroeconomic system boundary is drawn in the wrong place so its models cannot address many important issues

  • Gregory A Daneke says:

    Thank you for this insightful contribution, this is exactly the type of discussion I would hope for. I would recommend to all who are even remotely interested in Complexity to read the following spell-binding piece that just came out of a conference at Santa Fe. Yet, I remind you that despite Doyne’s occasional success with Wall Street, Complexity remains in the dog house with mainstream academic economists. And, be careful what you wish for regarding machine learning. Long live the evolution revolution. Cheers: Greg

    • Joachim H. Spangenberg says:

      Dear Greg,
      thanks for the compliments. The linked paper you recommend, however, has little to say about complexity as it mainly deals with an extended approach to evolution, including the noosphere.
      For readers looking for an extended basic introduction including an explanation of terminology, the iff text recommended by Ekke Weis (link in his comment) is indeed a very good entry point.
      Ciao, Joachim

      • Yoshinori Shiozawa says:

        Dear Greg,
        thank you for the link to the article: How social and physical technologies collaborate to create. I knew that Beinhocker uses the term “social technology”. I believe there is another important technology at the side of physical and social technologies. It is human technology. Examples are writing, reading, silent reading, calculating, and finally computer literacy.

  • Ekke Weis says:

    Thanks & Praises, & Maximum Respect for this piece!
    We all know that the World, and the World Economy are Complex Adapting & Evolving Systems which have been edging towards SUPER-CRITICALITY, i.e. are increasingly veering towards super-critical states.

    The key Factors generating such developments are always Endogenous Growth Factors and Growth Processes transcending Critical Thresholds (“Trigger Events”) – Population, Capital, Technological Change & Disruptive Technologies, increasing Interconnectedness and Network Effects, et al.

    For More on this, see (:>)
    > Per Bak (1997) How Nature Works: The Science of Self-Organized Criticality, Oxford University Press.
    > W. Brian Arthur (1994) Increasing Returns and Path Dependence in the Economy, University of Michigan Press.
    > Ekke Weis (2008) Fundamentals of Complex Evolving Systems: A Primer (=Working Papers 104), IFF/SOCEC (Institute for Social Ecology, Vienna):

  • Michael Joffe says:

    I liked the article – it is a nice summary of the challenge of complexity thinking applied to the economy.
    I have a question. I have no problem with the idea that conventional thinking in economics needs to be replaced. The question is, with what? (which in principle could be with “one thing”, or with many – “pluralism”. I leave that open.
    The question is, what is gained by adopting a “complexity” perspective, beyond analysing the economy in terms of causal systems that have feedback? Most of the regularities we see in the economy are eminently explicable using feedback concepts – not as deterministic models, but rather as indicating the sorts of causal processes that lie behind the various phenomena we see. These include the relative stability of some types of market, e.g. well established goods and nonfinancial services, like washing machines. Secondly, periodic fluctuations, as in real estate markets, and at a macro level with the business cycle. These are both balancing (negative) feedback, the second one with delay added. Thirdly, multiple equilibria (as Brian Arthur’s work shows) that leads to suboptimal technologies and lock-in. Fourthly, crises, booms/busts etc. Fifthly, economic growth that results from arms-race competition between “capitalist” firms. These last three are distinct types of reinforcing (positive) feedback. All these types of system can readily be illustrated using causal diagrams with feedback loops, that are rather easy to understand. Admittedly they assume an average or aggregate behaviour, so underestimate heterogeneity – but they do take account of causal interactions, which are the main driving force.
    What does “complexity” add to this perspective? – given that it tends to get into rather abstract theorising that is hard to understand (for most people), and has rather few concrete things to say about actual reality? This is a genuine question – I’m not at all saying it’s not useful, I just want help in identifying exactly what it delivers.
    Thanks, Mike

    • Yoshinori Shiozawa says:

      Dear Mike Joffe,

      complexity is another side of bounded rationality. It is shame that few people think about the significance of bounded rationality in a complex situation. People in Santa Fe talk much about physical properties of complexity like lock-in, non-linearity, positive feedback, multiple equilibrium, and emergence but not about the consequences of bounded rationality. Economics is a social science that studies human interactions. It is inevitable that new economics considers on human behavior in complex situations. It is almost equivalent to consider the meaning of bounded rationality for human behavior.

      Please read my paper Microfoudnations of Evolutionary Economics i.e. Chapter 1 of a book with the same title.

      If you send me your e-mail address to, I will send you PDF of the chapter in return.

    • Joachim says:

      Dear Mike,
      of course is true that we have a lot of interacting positive and negative feedback loops in the economy, plus delay etc. which shown in a simple diagram can help people overcome a static view on equilibria and recognise the dynamic. When I use them I emphasise three levels of the economy: the physical economy of resources, where e.g. the amount of harvest is changed by the climate crisis, or biodiversity loss. This makes the resource supply inherently unpredictable and drives prices up and down – unlike the standard models where supply is unlimited if you only pay enough. The resources are processed in the real economy, with all the elements you have described, and the revenues feed the virtual economy of money, with its inflated bubbles. Look at the loss of value of companies over the last week although they have not a single piece of equipment less, or at the value of companies which never earnt a cent, like it was long time for Tesla or the fracking industry. Complexity theory tells us that these systems are coupled, but run at different speeds with usually the fastest running system (i.e. finance) leading the others. That is, until this lead collides with their inherent structural conditions, resulting in crises. I think understanding these processes offers a significant amount of policy operating space, more than standard models and also some more than simple system dynamics descriptions.
      Finally, complexity theory adds two more elements to the feedback loop approach: as complex systems are not only dynamic but also evolving it highlights the snapshot character of and feedback loop system we are pinning down. And as evolution is not predictable (some of the reasons we described in our comment), we have to deal not only with risk (stochastic variations around a deterministic outcome), but also with uncertainty and ignorance (known and unknown unknowns). As a result, it is hardly possible to create credible economic forecasts for more than the next 20 years – and due to uncertainty, events like pandemics happen all forecasts become junk. That is why we have been advocating complementing model scenarios which are more or less linear extrapolation by shock scenarios, imagining single events changing the development trajectory. We even had a pandemic scenario, and the current crisis validates it – it looks like a blueprint. Had such elaborations been taken more serious when we published them a decade ago, societies might have been better prepared for the current shock. And that is when complexity becomes very down to earth.
      Best wishes, stay home & healthy

  • Yoshinori Shiozawa says:

    Your account of “three systems” seems too rough even if you have picked the idea from Sayer or Spash. The real-world system and two others are categorically different, because real-world system is the object of study /research / investigation. Mental model is one of the most primitive theories. Computer models are, as you point it, rather tools to test or check theories that are difficult to draw conclusions by logical reasoning.

    However, I believe your idea on computer models gives us important hints on our research strategy. I have coined a term for it: three modes of scientific research. The first is theory or logical speculation (“theory” came from Greek word “speculate” or “look at silently”). The second mode is experimentation. This is modern concept that appeared more than one thousand years later than theory mode. The third mode is computer simulation.

    For details, pleas see my paper: A Guided Tour of the Backside of Agent-based Simulation

    The second link gives you a draft of the paper which lacks Section 2, but you can read the essential arguments on the third mode of scientific research. If you want to read the published version, please give me an e-mail through ResearchGate as you and I are its members. I will be happy to send you a PDF of the paper.

  • Oliver Kovacs says:

    Please, feel free to express your opinion over this book which has been just recently published at Routledge:

    I do hope that some of you may stimulate further discussion, since if we do not have dialogue with colleagues, we can only have results we want.

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