Delta Scan: The Future of Science and Technology, 2005-2055: More Accurate Modeling of Complex Economic Systems

Key Pages

Category:
Science and Technology

Domain:
Economics, Complexity

Keywords:
Business Models - economics, simulation, complexity, psychology, grid computing

Outlook:
Advances in simulation tools and behavioural analysis may facilitate innovation in economic research methods.

Summary Analysis:
Within 10 to 20 years, agent-based modelling may facilitate a significant improvement in the scope and utility of economic models. Accurate simulations could potentially give economists new confidence in their conclusions. The difference this could make to economics can be compared with the impact of structural engineering on building. In the past, people designed buildings by intuition, experience, and guesswork. Today novel structures can be built with confidence because we have theories of structures and materials and we can model buildings with computers before they are built. Agent-based modelling could have a similar impact on economics.

Two factors are driving change in economics: accessibility of computational power and the adoption of behaviourist approaches. The (increasing) accessibility of computational power permits the accurate and rapid modelling of complex economies. The availability of computational power may, for a new generation of researchers, facilitate a shift in economics from deductive formalism to an applied mathematical approach based on simulations. In addition, the fundamental simplifying assumptions underlying current economic theory (greed, rationality, and equilibrium) are tending now to be replaced by new insights from behavioural studies of economic actors (firms, consumers, etc.) Drawing upon psychological experiments, behavioural approaches to economics may provide a far more accurate model of economic behaviour, revealing how variations in behaviour contribute to complexity in economic systems.

The impact of behavioural approaches to economic theory and computer power combined, may provoke a widespread shift in economic research methods from rational-actor models towards large simulations of complex economies inhabited by behaviourally-sophisticated agents. Cheap grid computing power will allow massive experiments that will begin to explain many of the seemingly random everyday trends in complex economic systems like the financial markets and international trade.

Implications:

Ability to explore a far wider range of public policy alternatives than previously
Creation of large new areas of inquiry for economists
Potential fo expansion of economic theories and analytical techniques in other social sciences

Early Indicators:

Awarding of recent Nobel prizes that lay the foundation for doctoral student interest behavioural economics
Increase in the number of doctoral dissertations having to do with agent-based modeling techniques, and increase in the funding of research taking these approaches
Work by a consortium of universities in a project known as NEW-TIES -- New and Emergent World models Through Individual, Evolutionary, and Social Learning -- on developing a large-scale and highly complex computer-based society as a means to understand social learning and behaviour

What to Watch:

Faculty research in economics reflects increasing interest in agent-based models and behavioural analysis.
Graduate economics curricula change to encompass agent-based models and behavioural analysis.
Results from behavioural/ABM analysis are increasingly used in policy debates.

Parallels/Precedents:

Complexity revolution in the "hard" sciences -- physics, biology, and computing

Enablers/Drivers:

Increasing availability of cheap desktop computational power
Continued development of high-level programming languages for building models
Continuing or expanding support for interdisciplinary work between economists and computer scientists, mathematicians, and psychologists
Neurotechnology for gathering data about emotional state of experimental subjects

Leaders:
Institutions:

University of Surrey, UK, Napier University, UK, Vrije University, The Netherlands, University of Budapest, Hungary (participation in the NEW-TIES consortium) [link]
Complexity Institute, London School of Economics [link]
The Complex Systems Network of Excellence (Exystence) [link]
The Complexity Society, UK [link]
New England Complex Systems Institute
Santa Fe Institute
Middlebury College

Figures:

Sources:

Camerer, Colin and George Loewenstein. 2002. "Behavioral economics: past, present and future." in Advances in Behavioral Economics. Princeton: Princeton Press.
Kahneman, D. & Tversky, A. "Prospect Theory: An Analysis of Decision under Risk." Econometrica, XVLII (1979), 263–291
Colander, David. "The Complexity Revolution and the Future of Economics." Middlebury College Economics Discussion Paper No. 03-19. August 2003.
Brown, Mike, Zoe-Vonna Palmrose, Warren Miller. 2005. Thog's Guide to Quantum Economics: 50,000 Years of Accounting Basics for the Future. Thogian Press.
Eve Mitleton-Kelly (ed.) "Complex Systems and Evolutionary Perspectives of Organisations: The Application of Complexity Theory to Organisations".
Fent T "Report from Vienna Topical Workshop on Agent-Based Computational Modelling. February 11, 2005 [link]
Lynch, Zachary. "Lecture at the Institute for the Future," Palo Alto, CA. 3 Aug 2005.
Complexity and Emergent Behaviour in ICT Systems [link]
Intelligent Distribution and Logistics [link]
Users and Services in Intelligent Networks [link]

At A Glance:
When:
11–20 years

Where:
Global

How Fast:
Years

Likelihood:
Medium-High

Impact:
Medium-Low

Controversy:
Medium

Related Outlooks:

About this outlook: An outlook is an internally consistent, plausible view of the future based on the best expertise available. It is not a prediction of the future. The AT-A-GLANCE ratings suggest the scope, scale, and uncertainty associated with this outlook. Each outlook is also a working document, with contributors adding comments and edits to improve the forecast over time. Please see the revision history for earlier versions.

Category:	Science and Technology
Domain:	Economics, Complexity
Keywords:	Business Models - economics, simulation, complexity, psychology, grid computing
Outlook:	Advances in simulation tools and behavioural analysis may facilitate innovation in economic research methods.
Summary Analysis:	Within 10 to 20 years, agent-based modelling may facilitate a significant improvement in the scope and utility of economic models. Accurate simulations could potentially give economists new confidence in their conclusions. The difference this could make to economics can be compared with the impact of structural engineering on building. In the past, people designed buildings by intuition, experience, and guesswork. Today novel structures can be built with confidence because we have theories of structures and materials and we can model buildings with computers before they are built. Agent-based modelling could have a similar impact on economics. Two factors are driving change in economics: accessibility of computational power and the adoption of behaviourist approaches. The (increasing) accessibility of computational power permits the accurate and rapid modelling of complex economies. The availability of computational power may, for a new generation of researchers, facilitate a shift in economics from deductive formalism to an applied mathematical approach based on simulations. In addition, the fundamental simplifying assumptions underlying current economic theory (greed, rationality, and equilibrium) are tending now to be replaced by new insights from behavioural studies of economic actors (firms, consumers, etc.) Drawing upon psychological experiments, behavioural approaches to economics may provide a far more accurate model of economic behaviour, revealing how variations in behaviour contribute to complexity in economic systems. The impact of behavioural approaches to economic theory and computer power combined, may provoke a widespread shift in economic research methods from rational-actor models towards large simulations of complex economies inhabited by behaviourally-sophisticated agents. Cheap grid computing power will allow massive experiments that will begin to explain many of the seemingly random everyday trends in complex economic systems like the financial markets and international trade.

	Implications:	Ability to explore a far wider range of public policy alternatives than previously Creation of large new areas of inquiry for economists Potential fo expansion of economic theories and analytical techniques in other social sciences
	Early Indicators:	Awarding of recent Nobel prizes that lay the foundation for doctoral student interest behavioural economics Increase in the number of doctoral dissertations having to do with agent-based modeling techniques, and increase in the funding of research taking these approaches Work by a consortium of universities in a project known as NEW-TIES -- New and Emergent World models Through Individual, Evolutionary, and Social Learning -- on developing a large-scale and highly complex computer-based society as a means to understand social learning and behaviour
	What to Watch:	Faculty research in economics reflects increasing interest in agent-based models and behavioural analysis. Graduate economics curricula change to encompass agent-based models and behavioural analysis. Results from behavioural/ABM analysis are increasingly used in policy debates.
	Parallels/Precedents:	Complexity revolution in the "hard" sciences -- physics, biology, and computing
	Enablers/Drivers:	Increasing availability of cheap desktop computational power Continued development of high-level programming languages for building models Continuing or expanding support for interdisciplinary work between economists and computer scientists, mathematicians, and psychologists Neurotechnology for gathering data about emotional state of experimental subjects
	Leaders:	Institutions: University of Surrey, UK, Napier University, UK, Vrije University, The Netherlands, University of Budapest, Hungary (participation in the NEW-TIES consortium) [link] Complexity Institute, London School of Economics [link] The Complex Systems Network of Excellence (Exystence) [link] The Complexity Society, UK [link] New England Complex Systems Institute Santa Fe Institute Middlebury College
	Figures:
	Sources:	Camerer, Colin and George Loewenstein. 2002. "Behavioral economics: past, present and future." in Advances in Behavioral Economics. Princeton: Princeton Press. Kahneman, D. & Tversky, A. "Prospect Theory: An Analysis of Decision under Risk." Econometrica, XVLII (1979), 263–291 Colander, David. "The Complexity Revolution and the Future of Economics." Middlebury College Economics Discussion Paper No. 03-19. August 2003. Brown, Mike, Zoe-Vonna Palmrose, Warren Miller. 2005. Thog's Guide to Quantum Economics: 50,000 Years of Accounting Basics for the Future. Thogian Press. Eve Mitleton-Kelly (ed.) "Complex Systems and Evolutionary Perspectives of Organisations: The Application of Complexity Theory to Organisations". Fent T "Report from Vienna Topical Workshop on Agent-Based Computational Modelling. February 11, 2005 [link] Lynch, Zachary. "Lecture at the Institute for the Future," Palo Alto, CA. 3 Aug 2005. Complexity and Emergent Behaviour in ICT Systems [link] Intelligent Distribution and Logistics [link] Users and Services in Intelligent Networks [link]

At A Glance:	When:	11–20 years
	Where:	Global
	How Fast:	Years
	Likelihood:	Medium-High
	Impact:	Medium-Low
	Controversy:	Medium