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Intentionality and the emergence of complexity: an analytical approach

Abstract

Emergence is a generic property that makes economies become complex. The simultaneous carrying out of agents’ intentional action plans within an economic system generates processes that are at the base of structural change and the emergence of adaptive complex systems. This paper argues that goals and intentionality are key elements of the structure of rational human action and are the origin of emergent properties such as innovation within economic complex systems. To deal with the locus and role of goals and intentionality in relation to the emergence of complexity we propose an analytical approach based on agents’ action plans. Action plans are open representations of the action projected by agents (as individuals or organizations), where the means (actions) and objectives (or goals) are not necessarily given, but produced by agents themselves.

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Notes

  1. 1.

    Emergence is a key generic property that makes economies become complex (Harper and Endres 2012).

  2. 2.

    A good classic precedent in philosophy is Ascombe (1957). An interesting approach quite complementary to ours is Bratman’s (1987 [1999], 1999). (See also Bratman et al. (1988).) An interesting review is Zimmerman (1989).

  3. 3.

    In a quite related field, Arthur (2007, 2009) has stressed the purposeful character of (actions that give rise to) invention and technical development.

  4. 4.

    Fuster (2003, 2008) physiologically locates action plans in the prefrontal cortex of humans.

  5. 5.

    For example a family’s travel plans, the business or production plans of a company, etc.

  6. 6.

    The concept of action plan has been used with different formalization by economists as diverse as Lachmann (1994 [1976]), Keynes (1936), Hicks (1939), Stackelberg (1946 [1943]), Barnard (1938), Debreu (1959), Penrose (1959), Malinvaud (1999), Boulding (1991), etc.

  7. 7.

    Culture, defined as in North (2005), plays a fundamental role in economic change.

  8. 8.

    Investigation cannot pre-exclude plans that contain systems of goals that are internally inconsistent. In fact, these kinds of plans may form part of the reality under study and constitute an interesting field of study in themselves. See for example Encinar (2002).

  9. 9.

    As far as plans are components of a bundle, and are intrinsically linked together forming a whole course of action, each pattern of bundling may be understood as an attempt at tentative modularization of action by the agent. The plans that form the bundle (three plans in Fig. 2) would be themselves quasi-decomposable modules of a higher level “system” of actions-goals –the bundle- that would direct the future course of action of the agent. For the meaning of quasi-decomposability and modularization see, respectively, Simon (1962) and Langlois (2002).

  10. 10.

    The new goal psychology represents a step forward in the integration of motives for action with psychological theories, generally cognitive, on human action. The links are the very goals or objectives of the action. See various chapters in the Oxford Handbook of Human Action (Morsella et al.2009), especially those included in part 2 (dedicated to the activation, selection and expression of action) and in Moskowitz and Grant (2009). On motivation in Economics see Frey and Jegen (2001) and Gerschlager (2012).

  11. 11.

    This bundle in a neoclassical account would roughly correspond to the bundle that maximises some objective function (utility, profits, etc.). However, in a more general (and realistic, that is, where true uncertainty prevails) framework the agent chooses bundles that “meet targets of adequacy rather than pinnacles of attainment” (Earl 1983: 78–81). It is very interesting to compare our analytical stages with those proposed respectively by Earl and Potts. The former (Earl 1983: 149-150) presents a multistage process in which the agent proceeds sequentially as follows: (1) problem recognition (a failure to match up to aspirations), (2) search of (not given) courses of action, (3) evaluation of possible sequels of particular choices, (4) choice itself, (5) implementation (often difficult and partially accomplished), and (6) assessment (the agent examines to which extent what was decided was achieved). Potts (2000: 120–123) addresses the problem of acting in a non-integral space. Agents must form conjectures as a solution by means of searching among adjacent possibilities which relationships may solve (are more promising ways of solving) their particular problems. The ‘decision cycle’ that makes these operations possible consists of four separate components: {LIST, CONSTRUCT, RANK, SELECT}. The main point in Potts’ proposal is that, for him, these conjectures are the agents’ preferences (note the conjectural character of action plans).

  12. 12.

    The ensemble refers to the “reality” such as it is conceived by the agents in order to produce their action.

  13. 13.

    The term “beliefs” refers to the set of conceptions, representations and knowledge to which the individual is faithful. In general, beliefs imply evaluation criteria that organize the projective action and the action of decision among alternatives and value judgments. “Values” is understood as the set of valuation criteria effectively used by the individual to projectively organize the action and issue value judgments. The possible difference between the valuation criteria implied by the beliefs and those effectively used in practice must be acknowledged. Values include tastes and preferences. “Attitudes” refers to stable features that introduce determination in certain aspects.

  14. 14.

    In our approach, the symbol → neither represents a logical relationship (for example a material conditional if-then relationship) nor a mathematical function that relates two (or more) variables (as is the case of a production function, for instance). It designates a mode -that is, a conventional sign- of representing a necessary causal relationship among theoretical structures. Quite a different issue is that in some very specific circumstances it is possible to characterize parts of a theory of human action by means of proper (not imposed) mathematical structures as is the case of neoclassical economics under highly restrictive theoretical assumptions.

  15. 15.

    This stage is rather similar to the one that Earl (1983: 150) has called “assessment” in his multistage process model of choice.

  16. 16.

    The agent’s state may include explicitly the agent’s “biography”; the set of all the states of all the individuals other than i prior to time t that may influence the agent.

  17. 17.

    They could be unplanned actions due to, for example, the use of routines, rules, procedures and behavioural habits, which also generate consequences, expected or otherwise, in action.

  18. 18.

    At t it may be that n t n t−1 or that n t <n t−1.

  19. 19.

    How individuals set goals, etc., is very important for other disciplines such as psychology. (See for instance, Ajzen 1991; Miller et al. 1960; Moskowitz and Grant 2009).

  20. 20.

    New in the sense of “unheard-of”.

  21. 21.

    Owing to this, novelties cannot be uncaused causes as Hodgson (2004, chap. 3) suggests: the ultimate cause is the intentionality of agents. In an example provided by Schumpeter’s (2005 [1932]) Mantegna’s innovations could be interpreted as a conscious and individual act undertaken by the painter; the ‘Renaissance style’ produced unexpected innovations in painting as a result of painters interactions.

  22. 22.

    As has been said above, in economics the simplest example of ΔS is a perfect-competition market; in this structure of interaction –in which agents, agents’ goals, and structure of interaction do not change- the consequences ΔS are expressed in terms of the complex of produced and consumed quantities and the equilibrium price. Of course ΔS may be more complex: we may think that there is rationing equilibria (Benassy 1986; Malinvaud 1977); non-market interactions (Schelling 1978); network effects (Katz and Shapiro 1994); etc.

  23. 23.

    Examples include the analysis of the origin and evolution of techno-economic innovation systems, the emergence of technological clusters, the evolution of institutions, etc.

  24. 24.

    The role of beliefs, etc. has been recognised in economic theory. Recently, Acemoglu has pointed out that the fundamental causes of economic growth are luck, geographical differences, institutional differences and “cultural differences that determine individuals’ values, preferences and beliefs” (Acemoglu 2009: 20).

  25. 25.

    It must be stressed that the aim of this paper is not to offer a technical solution to a particular problem. It is an analytical proposal intended to make tractable North’s (2005) and Loasby’s (2012) challenges, i.e.: to erect scaffoldings (analytical frameworks) that allow us to deal with human interaction and the sources of complexity (and structural change) -scaffoldings being able to accommodate, at the same time intentionality and different “ecologies of plans” (Wagner 2012).

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Acknowledgments

The authors would like to thank two anonymous referees for their very useful comments and suggestions as well as to Professor Rafael Rubio de Urquía, Carolina Cañibano and David Harper, for their valuable advises.

The authors acknowledge the scientific support from the ‘Instituto de Investigaciones Económicas y Sociales Francisco de Vitoria’ and Universidad Aut ´onoma de Madrid. The usual disclaimers apply.

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Muñoz, FF., Encinar, MI. Intentionality and the emergence of complexity: an analytical approach. J Evol Econ 24, 317–334 (2014). https://doi.org/10.1007/s00191-014-0342-z

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Keywords

  • Intentionality
  • Action plans
  • Emergence of novelty
  • Complexity

JEL Classifications

  • B41
  • B52
  • D89
  • O10