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When Minsky meets complexity

by Kimberley Yoo on Mar 29, 2018
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My thesis ‘Time to Rebuild and Reaggregate Fluctuations’ stands on the shoulders of giants; giants like J. Barkley Rosser Jr. and Basil Moore, who worked to close the gap between complex systems science and Post Keynesian economics. The thesis builds on this effort by using Hyman P. Minsky’s theories of endogenous aggregate cycles as a central waypoint to complexity. It is an unabashed attempt at a synthesis between Minskian economics and the transdisciplinary project of complexity theory.

There must be a preamble on what this complexity theory entails. Complexity theory is an umbrella moniker for a broad field concerned with the Aristotelian claim that ‘the whole is greater than the sum of its parts’. The brain, immune system, ecologies and the entire universe can be considered complex systems, where a fundamental unit (e.g., a cell or atom) somehow creates organised macro-phenomena irreducible to the unit itself. By nature of the sheer range of complex systems, complexity science is a transdisciplinary project of the physical and social sciences. It attacks the core of the structure/agency dualism of the social sciences, by exploring how the level mediating the two connect through individual interaction. In economics, it is no longer about how micro principle-agent problems manifest in aggregate macroeconomics, but about how a meso-level characterised by social and spatial networks propagate often strange systemic dynamics.

The problem is that there has been little rigorous exploration on how this movement of complexity is connected to economic theory, or consideration over what ontological and epistemological statements are being made when adopting complex systems analysis. Minsky’s oeuvre is particularly useful because the dynamics espoused are empirically and theoretically commensurable with the literature of complexity economics, as well as being situated within the social philosophy of Keynes. Yet, a synthesis may be considered a useful intervention if deficiencies in the respective theories are addressed through theoretical comingling. The thesis therefore addresses several underdeveloped areas in Minsky’s conception of endogenous cyclical fluctuations:

  1. Minsky is liable to the fallacy of composition by using the representative agent under linear programming methods;
  2. ‘Uncertainty’ and ‘animal spirits’ are often used as a catch-all retorts to explain the self-fulfilling nature of expectations in aggregate cycles; and,
  3. There is no meaningful attempt by Minsky to model the mechanisms detailed, because the theory necessarily captured unwieldy dynamics beyond the scope of economic methodology at the time.

Before providing an example of how these issues can be resolved, there must be an equally critical eye cast on complexity science. Indeed, a large motivator for ‘Time to Rebuild’ is the offensive lack of social philosophy in complexity economics, which restricts the domain of analysis to the point that it may as well be called ‘Neoclassicism Mark II’:

  1. An influx of physicists in economics has created an ‘econophysics’ sub-discipline in complexity that supports many heterodox suppositions but does not acknowledge it (qualification of this is found in the aforementioned thesis);
  2. The philosophical offshoot of complexity theory draws from post-structuralist philosophy, although there is scant reference to this in applied complexity theory; and,
  3. Complexity theory is not an economic theory and does not have a coherent set of precepts unique to the study of economic systems.

These points are brought forward to say that Minsky provides a rigorous theoretical base in political economy that complexity lacks, while complexity forms a robust systems framework to the formal ambiguities of Minsky. Although I am strapped for time here, I would be remiss to not mention two points of interest. A complexity-Minsky synthesis retains the ontology of an ‘open system’ but admits the impossibility of a holistic general theory under the principle that perspective determines what is observed. As a result, radically positivist approaches are untenable, but the methodology itself is not problematic if the ontic-epistemic considerations are explored. This means that formal modelling devices developed under complexity sciences, such as agent-based modelling (discussed in the thesis), are useful pedagogical and policy devices, but cannot be taken as a complete representation of reality. This last point may seem so obvious it’s tautological, but the history of economic thought proves that it must be continually reiterated.

To connect some of the dots, let’s return to the problem of theorising around recurrent upswings and downturns experienced in capitalist economies. How do individual decisions aggregate outward to produce such persistent movements in investment and output?

The work of Hyman Minsky would suggest that tranquillity breeds instability through the biases of firms under uncertainty. Uncertainty over the future state of the world leads to firms assuming their current situation will not change for the foreseeable future. Buoyant macroeconomic conditions, characterised by strong aggregate demand and minimal risks, can therefore encourage the accumulation of debt under optimistic expectations. This motivates speculative upswings which precede a potentially brutal market correction. Yet, there is no a priori reason for a collective increase in debt unless we assume an oligopolistic or monopolistic market. If it is accepted that firms are heterogeneous in all regards, it may be the case that some firms pull back investment and others expand. In the aggregate, the rise and fall of leverage in many firms may normalise to zero and there would be no impact in the macroeconomy. To answer this critique with `animal spirits’ requires a qualification of what role uncertainty has in shaping decision making, as well as how speculative sentiment traverses along a population.

The answer lies somewhere within the mesoeconomic space, where the network between individuals is of primary concern. The technical argument of the thesis is as follows. Aggregate fluctuations and volatility arise from the financing decisions of firms, who face a fundamentally uncertain future. To mitigate this, firms use heuristic rules, e.g., following what the majority do, or assuming their current situation continues indefinitely. The adoption and dissemination of these heuristics depend on the architecture of their social and spatial network. If these rules are accepted to guide investment behaviour, speculative leveraging decisions can propagate contagion effects via networks, resulting in cycles driven by optimistic and pessimistic sentiment. As a result, what may be conventionally seen as a ‘rational’ decision at the microeconomic level, may create an emergent maladaptive (e.g., Fisherian debt deflation) outcome if most agents follow suit. Social networks are then formal mechanisms that transmit speculation, and the network topology is a source of endogenous volatility, which is irreducible to the firm itself.

It makes sense that a theory concerned with complex phenomena like endogenous cycles would be sensitive to this complexity itself. The thesis concludes with a warm invitation to other traditions in political economy to explore the world of complex systems, summarised here through Basil Moore:

…we must reject linear equilibrium analysis completely and replace it with process analysis, and eventually computer simulation of nonlinear systems. As I have read more deeply into the literature of complexity I am continually amazed how well it fits my personal perception of reality. I warmly invite others to come down the same path. But please, get down behind a boulder. – Basil Moore, ‘Shaking the Invisible Hand’, pp. xv.

Kimberley Yoo
Kimberley Yoo is a PhD candidate at the University of Sydney researching computational modeling and complexity theory. Her research interests include heterodox financial macroeconomics, the political economy of game theory and mechanism design, and computational economics.

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