Holism: the relevance of downward effects

Wilber and Harrison (Reference Wilber and Harrison1978) explicitly distinguished between holism and systemism. Holism is considered to be the opposite of atomism and entails a focus on the pattern of relations among the agents and the economy as a whole (Wilber and Harrison, Reference Wilber and Harrison1978, p. 71). This expresses the belief that the whole is not only greater than the sum of its parts, ‘but that the parts are so related that their functioning is conditioned by their interrelations’ (Wilber and Harrison, Reference Wilber and Harrison1978, p. 71). Here it is important to distinguish among ontological and epistemological holism: If holism has an ontological meaning, the study of agency, individual incentives and the relation among the parts making up the whole becomes unnecessary. Such a view must not be compatible with institutionalist theory. Institutionalists have always stressed the learning capacities of individuals, the variety of reasons guiding their decision making, their instincts and their idle curiosity. Such concepts are worthless to the ontological holist as they would be mere derivatives of the social structure in which the individuals exist. More adequately, holism is understood in the epistemological sense: In order to understand the behaviour of individuals, a deep understanding of the social structure into which they are embedded is required. This is what Wilber and Harrison mean when they argue that the process of social change is the product of human action, which itself is shaped and limited by the societal structures it is embedded into. While their distinction to systemism is not clear, their use of the concept of holism suggests that both the relations among individuals and the relation between different ontological levels of the economy are important.

This idea is most precisely developed in the institutionalist concept of reconstitutive downward effects (Hodgson, Reference Hodgson and Fullbrook2002; Reference Hodgson2006; Reference Hodgson2011): individuals, groups and the entire population are strongly interconnected and patterns emerge because of this interconnectedness of different ontological levels. These emergent patterns then shape the consciousness and behaviour of the agents on the individual level again. They are independent of the support of the single agent but can only be sustained if they are supported by a critical mass of agents. Because these effects arise from the action on the lower micro level, but these actions are influenced by the effects themselves, they are called reconstitutive downward effects. Following the current conventions, a theory considering reconstitutive downward effects would not be termed holistic, but systemic. Wilber and Harrison (Reference Wilber and Harrison1978) made use of the term holism probably as a differentiation to neoclassical individualism. But individualism is also rejected by systemism alone (Bunge, Reference Bunge2000).

Can ABMs be consistent with a view of the economy that stresses the mutual interdependence of its different layers? Brian Arthur, a leading figure of the complexity movement in economics and an advocate of ABMs, described them as models in which ‘[b]ehaviour creates pattern; and pattern in turn influences behaviour’ (Arthur, Reference Arthur, Tesfatsion and Judd2006, p. 1553). This is the same as to say that ‘parts are at once conditioning and conditioned by the whole’ (Wilber and Harrison, Reference Wilber and Harrison1978, p. 80). In an ABM, one specifies the agents and how they behave in certain situations. The trigger for their behaviour can be, as explained above, their own state, the state of their direct environment, the state of a certain group or the state of the global system. As other agents, groups and the system as a whole are also influenced by the agent herself, it is straightforward to see how the concept of interdependent levels can be accounted for in ABMs.

One could also deal with a more refined version of such a layered ontology within an ABM: Building on the ontology of evolutionary realism of Dopfer and Potts (Reference Dopfer and Potts2004), Dopfer et al. (Reference Dopfer, Foster and Potts2004) propose a micro–meso–macro framework which puts the diffusion of rules at the centre stage: In their analytical frame, a meso unit is not simply a subset of the whole population, but a rule and all its instantiations in the population of agents. The micro level is represented by the economic agents, who are users and carriers of rules, and the macro level is the interrelation of different rules (deep structure) and the corresponding carrier groups of the rules (surface structure). Here, the technical question of whether either agents or rules or both should be modelled as classes on their own, forces one to be extremely precise on the theoretical level and to elaborate on small but significant differences among different ontological and analytical approaches.

Systemism: organised complexity and self-organisation as central properties of social systems

For Wilber and Harrison (Reference Wilber and Harrison1978, p. 71) systemism meant that ‘parts (of a system) make up a coherent whole and can be understood only in terms of the whole’. This conception of systemism is now outdated. The idea the authors convey is that the relations among the entities of the system under investigation matter and can lead to ontological novelties (which then may feed back on the related entities, see Section ‘Holism: the relevance of downward effects’).

This idea is captured by the philosophical framework elaborated by Mario Bunge. For Bunge, systemism means to understand everything as either a system or a part of one. The parts of a system are related in a particular manner, giving rise to emergent properties on higher ontological levels.Footnote ⁶ It is interesting that Bunge uses the work of Veblen, Myrdal and Schumpeter as examples for systemist research and thus points to the strong affinity of OIE and neo-Schumpeterian economics.

But even more importantly for the topic of this paper, there exists a strong theoretical affinity to the conception of complexity of Weaver (Reference Weaver1948). His concept of organised complexity is at the heart of complexity economics and suggests a strong complementarity among OIE and complexity economics. Weaver contrasts complex scientific problems from simple ones:

Simple problems include only few variables and were studied by pre-1900 physics and engineering. Problems involving living organisms can never fall into this category: they involve too many different aspects and, because of the interrelatedness of the variables, defy ceteris paribus assumptions (Weaver, Reference Weaver1948, pp. 537–538).

Weaver then distinguished between problems of organised and disorganised complexity: A system consisting of many components shows disorganised complexity if some emergent pattern exists because the linear interactions between the different elements smooth each other out. The Law of Large Numbers describes such an emergent pattern. Most econometric theory assumes this kind of complexity when it assumes error terms to be identically and independently distributed.

In contrast, a system showing organised complexity exhibits patterns, which emerge because the interactions of the different elements do not smooth each other out (i.e. are non-linear). This is the case if there exists a kind of self-organisation of the system such that the factors are interrelated into an organic whole (Weaver, Reference Weaver1948, p. 539). When arguing for the need of systemic models, institutionalists implicitly say that the economy exhibits organised complexity.Footnote ⁷ The analytical models of neoclassical economics presume the economy to show disorganised complexity. Their unambiguous results can only be obtained by assuming mechanical agents that interact in a linear fashion.

Many ABMs are motivated with the argument that the economy exhibits organised complexity (Miller and Page, Reference Miller and Page2007; Potts, Reference Potts2000). The implementation is straightforward: Heterogeneous agents interact with each other and their environment. As there is no requirement for the system to exhibit any particular dynamic (esp. an equilibrium path), assumptions can be made on entirely proper considerations. One can then conduct artificial experiments by changing an aspect of the model and check whether an emergent pattern is the result of the change or not. One can model the system with an adequate specification without a compulsive formalism, but with the obligation to state any assumed process explicitly.

Again, the model of Hodgson and Knudson (Reference Hodgson and Knudsen2004) is an excellent example: the authors have one version of the model with inertia instead of habit and thus can compare the effect of this different decision making algorithm on the overall dynamics. The resulting convention in their model is both dependent from individuals following it, but also influences individual behaviour.

Weaver's definition of organised complexity also suggests to relax or drop assumptions about fictitious central planning mechanisms such as the Walrasian auctioneer, but study the economy as an interactive and self-organising system without central control. The concept of self-organisation has been discussed by leading scholars of Schumpeterian economics such as Witt (Reference Witt1997) and Foster (Reference Foster2000).

It also plays a role in the discussion about Darwinian principles in economic theory: While some consider self-organisation as an alternative to Darwinian principles (Witt, Reference Witt1997), others claim that self-organisation is an important mechanism which has to be embedded into a broader Darwinian framework (Hodgson and Knudson, Reference Hodgson and Knudsen2010). No matter whose position is taken, the capacity of a system to organise itself without a central planner is considered to be important and to deserve attention.

Practically, this means that one has to deduce the overall dynamics from the interaction of its constituent parts. This is exactly what ABMs were invented for. The next step is to consider the precise structure of the interaction.Footnote ⁸

Social structure and networks

A systemist analysis of the economy requires one to pay attention to the relations within the economy (Bunge, Reference Bunge2004, p. 188). These relations are represented by networks (or graphs). Network science has been a lively area of research and developed both a plausible taxonomy for empirical networks and theories of how these networks could have come into existence.

Institutionalists should build on these insights: For most systems, ‘structure always affects function’ (Strogatz, Reference Strogatz2001), so one should be very precise about this structure. But networks are difficult to describe verbally and the relation between network structure and the economic outcomes is usually not intuitive. In this case, ABM is a very strong ally in visualising the mechanisms underlying real-world dynamics.

Such an application of ABM contrasts the implicit practice of neoclassical economics to assume complete (or trivial) networks, where all agents need to be the same. This can only be true in trivial (complete or empty) networks.

But recent studies of networks showed that small-world or scale-free networks are ubiquitous in reality.Footnote ⁹ Small world networks are characterised by small average path lengths between the nodes and comparatively high degree of clustering.

Roughly speaking, in small world networks the nodes may not interact directly with one another but the number of middle nodes required to connect two randomly chosen nodes is usually small. Additionally, there are groups of nodes that are very closely connected to each other, i.e. where the probability that there is a connection between two group members is extremely high. The constituent feature of scale free networks is that there are very few nodes with a lot of connections to other nodes, and very many nodes with very few connections to other nodes. More precisely, the distribution of the number of neighbours (i.e. the degree of the nodes) follows a power law.

How these networks influence the distributional properties of an economic system can be studied via ABM. Albin and Foley (Reference Albin and Foley1992), for example, considered changing network structures in the general equilibrium framework and showed how a shift from central to de-central organisation has severe distributional effects. Their model remained very abstract and one would not classify it as an institutionalist model. They exemplify, however, the huge consequences of a small change in the underlying network structure. This insight is important for institutionalists when describing the stratification of real-world economies, because networks play an important role for the observed stratification (and unequal distribution of wealth).

In order to figure out how this role looks like, one must build on a formal assessment of these network structures, particularly because network structures are a catalysator to other aspects (Page, Reference Page2012). To have these results in mind is important for the construction of purely verbal models as well. Economic networks of directly interdependent agents acting without any central control are ubiquitous in current times: The increasing fragmentation of valued added chains, the growing importance of network-based information and tele-communication technologies, and the ever more centralised industrial structure with few huge corporations and many smaller, globally dispersed, sub-contractors (and the resulting hub and spoke networks) make it essential to pay attention to the underlying network structure in the economic system under consideration. ABM can be an indispensable tool if one wishes to explain rather than just to describe the role of network structures: problems including network structures quickly become intractable in an analytical sense. Purely verbal models, on the other hand, are not accurate enough to capture important differences of various network structures, even in a qualitative way.

Evolution and agency

As an alternative to neoclassical equilibrium analysis, institutionalists developed concepts such as circular cumulative causation, path dependence and reconstitutive downward effects. Much appeal of ABM stems from the fact that they can constitute non-linear dynamical systems exhibiting such non-ergodic properties, based on intuitive (behavioural) assumptions (Arthur, Reference Arthur, Tesfatsion and Judd2006; Edmonds, Reference Edmonds1999). For neoclassical models (in the wider sense), it usually requires quite a bit of axiomatic variation (Kapeller, Reference Kapeller2011) to derive such properties within their optimisation-cum-equilibrium framework.

Evolutionary economists have already recognised the usefulness of ABM when developing the concept of ‘History-friendly models’ (Malerba et al., Reference Malerba, Nelson, Orsenigo and Winter2001; Orsenigo, Reference Orsenigo, Hanusch and Pyka2007). This concept tries to bridge the extremes of conducting very detailed and specific case studies, and to design abstract and general theories:Footnote ¹⁰ One starts with concrete case studies and then expresses the verbal arguments rigorously in a formal language. For such purposes, the formalism has to be sufficiently flexible to include sufficiently realistic assumptions. ABMs meet this criterion. The parameters of the resulting model get calibrated according to empirical observations or on the basis of sound theoretical assumptions. Then the model is used to assess the empirical consistency, robustness and generality of the verbal arguments by comparing the resulting dynamics of the model to the true dynamics of the case study. If one is able to replicate past behaviour of the system by having identified the central causal mechanisms, one can change the model in order to construct alternative histories, e.g. to assess certain policy interventions.

History-friendly models are a nice example of a concept that has been developed largely in the evolutionary economics community, but is highly relevant for OIE thinkers, e.g. for the study of institutional change. Murmann and Homburg (Reference Murmann and Homburg2001) stress that rigorous case studies are a prerequisite to proceed to the formal (history-friendly) model. OIE scholars have already developed many of such historically grounded case studies and can build upon this work. For this task, ABMs have proven successful (Malerba et al., Reference Malerba, Nelson, Orsenigo and Winter2001).

But the evolutionary flavour of ABMs is not limited to the aggregated level: The agents themselves are not static and rational, but can be boundedly rational and adaptive. Their reasoning is not necessarily deductive, but, following psychological evidence, can be inductive and based on heuristics. They are not isolated representative entities, but socially embedded agents (Edmonds, Reference Edmonds1999; Pyka and Fagiolo, Reference Pyka, Fagiolo, Hanusch and Pyka2007).

An adequate representation of the economic agents is not only an important tool to make models evolutionary in the institutionalist sense. It also contrasts the instrumentalist use of rational agents of neoclassical economics with a representation of the economic actor that involves an adequate level of descriptive accuracy. It helps to develop models that meet empirical findings on different ontological levels (Pyka and Fagiolo, Reference Pyka, Fagiolo, Hanusch and Pyka2007).

This aligns well with the research praxis of institutionalists not to stop with the postulation of a certain preference relation but to elaborate and justify their particular behavioural assumptions more concretely. More concrete motives include a thirst for power, altruism, an instinct of workmanship, idle curiosity, conspicuous consumption and time and space dependent habits (see Rengs and Wäckerle (Reference Rengs and Wäckerle2014) for a concrete example).

Again, there are strong convergences between OIE and modern evolutionary economics: Both seek a more realistic conception of economic agents and argue for assumptions justifiable by evolutionary theory, termed ‘the principle of evolutionary explanation’ by Hodgson (Reference Hodgson2004, p. 157).

Developing an adequate conception of agency for ABMs consistent with this principle represents an alternative research trajectory than that of neoclassical economics: realism of its agents is less important and a common reaction to criticisms of the utility-maximising homo oeconomicus has been the use of more and more complex utility functions (e.g. by including social preferences or by adding ‘decision defects’). Thus, it missed the essence of Herbert Simon's idea of bounded rationality, according to which agents reduce the complexity of their decision process due to a lack of computational capacity. They employ heuristics to cope with the complexity of their environment, rely on institutions and make decisions more inductively than deductively.Footnote ¹¹

ABMs can follow this more promising research trajectory using institutional and evolutionary theory. Already the work of Herbert Simon highlighted how adequately human reasoning can be represented via algorithms.Footnote ¹² More sophisticated decision procedures could be included using genetic algorithms. Genetic algorithms represent heuristics that help to solve optimisation problems in a satisfactory way if a straightforward maximisation is not feasible. It starts with a set of possible solutions to a problem, evaluates them according to a criterion, combines them randomly based on their performance and evaluates the resulting combinations again. By proceeding this way, the results usually become better and better. Such algorithms mimic the principles of natural selection and can explain not only many biological phenomena but also how certain instincts and behavioural habits have come into existence (Mitchell, Reference Mitchell1999).

When employed on agents, they can help to simulate the learning behaviour of agents and their way of adapting to their environmental requirements. This is one source for the ability of ABMs to describe non-reversible dynamics and to resemble the principle of (circular) cumulative causation and the path dependence of real world dynamics.

Newer trends in evolutionary and institutional economics

In addition to the core principles of institutionalist methodology discussed above, it is necessary to have a closer look on actual methodological trends within institutionalism.

Important methodological conclusions emerged from the discussions about the potential application of (evolutionary) game theory and system dynamics in institutionalist research: In both cases, the formal models were motivated by the observation that traditional, mainly verbal, pattern modelling does not allow for sufficient logical depth and rigour to address complex situations with different mechanisms playing an important role (Heap and Varoufakis, Reference Heap and Varoufakis2004; Radzicki, Reference Radzicki1988; Vilena and Vilena, Reference Vilena and Vilena2004). These arguments apply in a similar manner for ABM: it allows to formalise different mechanisms very precisely without coming up with an analytical straitjacket limiting the descriptive richness of the resulting model.

Another discourse is about a closer orientation of OIE on Darwinian core principles: Some argue that evolutionary economics necessarily have to be built upon the Darwinian principles of variation, selection and replication (e.g. Hodgson (Reference Hodgson and Fullbrook2002) or Hodgson and Knudson (Reference Hodgson and Knudsen2010)).

Others claim that these principles are of great importance to understand and define the framework conditions of economic activity (such as humans cognitive capacities, instincts, etc.), but are ill-suited to study the resulting dynamics and cultural evolution within the actual economic sphere (e.g. Witt (Reference Witt2004) and Cordes (Reference Cordes2006)).

None of the two positions is incompatible with the use of ABM. To the contrary: both approaches emphasise the importance of a realistic conception of economic agents, their habits, heuristics and cognitive abilities. As illustrated earlier, ABMs can be useful in this context. The same can be said of out-of-equilibrium dynamics and self-organisation.

In the end, none of the actual methodological trends changes the classical cornerstones fundamentally and all of them are likely to profit from the application of ABM.