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Complexity Economics: A Path Beyond Neoclassical Dogma

Explore the profound shift from Neoclassical Economics to Complexity Economics, diving into the concept of convexity, feedback mechanisms, and self-organization. Discover the role of institutions in shaping choices and the emergent properties of self-organizing systems. Delve into the dynamics of chaotic and complex systems, emphasizing the importance of information dispersion and relative variables in economic models.

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Complexity Economics: A Path Beyond Neoclassical Dogma

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  1. Sami Al-Suwailem IRTI - IDB

  2. Coined by Brian Arthur (1999) • Attracting more attention • Promising with great potential • Still needs more precise characterization

  3. Probably the most fundamental property of Neoclassical Economics (NE) • Convexity can be viewed as an organizing principle of the comparison between NE and CE • Any combination of two points in the set belongs to the same set

  4. Convexity precludes novelty by design: combinations always belong to the same set • No innovations or creativity • No learning or R&D • No entrepreneurship

  5. Convexity allows only negative feedback; positive feedback is not allowed • Equilibrium, static system • No dynamics, no self-organization

  6. Maximization requires convexity • Path-independence: Choice process or act of choice is ignored • No place for emotions, ethics, values, and social relations

  7. NE assumes perfectly divisible commodities • Real numbers exclude Diophantine problems • But if commodities are indivisible, we cannot use real numbers • Rational numbers are non-convex • Diophantine problems are not decidable

  8. Neoclassical Economics Complexity Economics • Novelty not allowed • Negative feedback • Maximization • Path-independent • Decidable • Perpetual novelty • Negative and positive • Satisficing • Path-dependent • Undecidable

  9. Institutions can be seen as factors for “convexifying” the choice set • Convexity might not be always bad! • A promising line of research

  10. Self-organization—structure • Emergence—function

  11. Global order via local interactions • Implies positive-feedback—excluded by convexity • Examples: • Bird flocks • Fireflies

  12. “The whole is greater than the sum” • Self-organizing system is able to perform functions the sum cannot • Implies novelty—excluded by convexity

  13. “Swarm Intelligence” • “Group Genius” • “Wisdom of the Crowds”

  14. Both exhibit nonlinear dynamics and universality • Different nature and properties

  15. Chaotic Systems Complex Systems • Indistinguishable from random behavior • Ergodic • Non-adaptive • Basin of attraction computable • Not capable of universal computation • Path-independent • Recognizable patterns and order • Non-ergodic • Adaptive • Basin of attraction not computable • Capable of universal computation • Path-dependent

  16. Fireflies, bird flocks, fish schools, … • Agents react to two sources of information: • Environment—exogenous variables • Local neighbors—endogenous variables

  17. Each agent has a piece of knowledge regarding target or objective • Relative variables communicate the knowledge to neighbors • As each agent adjusts to its neighbors, the whole group synchronizes to the environment • Knowledge therefore becomes integrated

  18. “The problem is thus in no way solved if we can show that all of the facts, if they were known to a single mind … would uniquely determine the solution; instead, we must show how a solution is produced by the interactions of people, each of whom possesses only partial knowledge”

  19. Dispersed information zi must be consistent • An aggregator function must exist: • Fneed not be computable • Agents need not know F but they need to know it does exist

  20. If independent variables dominate: stagnant order • If relative variables dominate: chaos • Complexity lies at the edge between order and chaos

  21. Relative variables: the “self” part • Independent variables: “organization” part

  22. Missing from mainstream NE • Supported by behavioral and social studies • Also by physical sciences (spin glass) • A point of departure of CE from NE • But can be a point for extending NE

  23. Consumption is presented as a function of income (and wealth) • How about consumption of local neighbors? • “Social capital” • “Social multiplier”

  24. As agents get different incomes, they cannot have equal consumption • How to react to “consumption gap”? • Move to different neighbors—Schelling model • Trade—labor market • Borrow—different modes of finance • Donate—philanthropic institutions

  25. Simple (NE): • Complex: • How this affects economic variables?

  26. NetLogo 3.1.4 • 1225 patches (agents); 8 neighbors • 1500 periods • 30 runs • Income is exogenous, uniformly distributed across agents; normally distributed across time • Gap is closed using interest-free lending

  27. Surpluses are managed centrally • Deficits are financed from accumulated surpluses • Loans are extended based on available funds

  28. Total assets = total cash + total credit • Total cash = cum (total-surplus + total-principal-payment – total-loans) • Net wealth (i) = share in total assets – debt • Share (i) = (acc. surplus) / sum (acc. surplus) • Total credit = total debt

  29. Desired Consumption surplus deficit Loans Loanable funds Consumption, wealth

  30. Sum (net-wealth) = total cash • Sum (shares) = 1 • Income + loan – consumption – surplus – installment = 0

  31. Smoother consumption • Higher wealth • More efficient fund utilization

  32. Consumption

  33. Net-wealth

  34. Wealth and Debt

  35. Flow of Funds

  36. Relative behavior smoothes out consumption • Smoothing reduces excessive consumption, thus adding to wealth • Connectedness facilitates channeling funds to deficit agents • Higher net-worth and higher efficiency

  37. Relative behavior deserves more attention • Agent-based simulation provides a rich environment for research • Extensions to investment and capital markets

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