Notes on Complex Systems and Evolutionary Trade Models (Part II. Complex Systems and Economics)

.
Link to this blog post: http://raffysaldana.blogspot.com/2009/09/notes-on-complex-systems-and_21.html

Note: Recently, the Ateneo de Manila University (AdMU) Innovation Center headed by Dr. Gregory Tangonan started a complex systems study group. This semester (1st semester, school year 2009-2010) the Electonics, Communications and Computer Engineering (ECCE) Department under the School of Science and Engineering offered a graduate course on "Complex Systems" with me as one of the facilitators (together with Dr. Greg Tangonan and Dr. Nathaniel Libatique, Chair of the ECCE).

My particular interest in the topic of complex systems is on modeling and simulation. As of this writing, I am involved in a research project dealing with complex systems dynamics of evolutionary trade models and international development.

To aid my students and my research group on complex systems (and other interested readers) I am putting online some of my notes on this topic.

NOTES ON COMPLEX SYSTEMS AND EVOLUTIONARY TRADE MODELS

(Note: This is a work in progress. References will be supplied later.)

Outline:
Part I. Introduction to Complex Systems
Part II. Complex Systems and Economics
Part III. An Evolutionary Theory of Trade
Part IV. Incorporating Technological Productivity in the Evolutionary Trade Model
Part V. A Cellular Automaton Model of Evolutionary Trade
Part VI. Non-Equilibrium, Spatial Evolutionary Trade Models
Part VII. Case Study: Modeling the Impact of Changes in Transportation Cost in India
Part VIII. Case Study: A 'Policy Impact Model' for Nepal
Part IX. Agent-Based Computational Economics

Part II. COMPLEX SYSTEMS AND ECONOMICS

Matutinovic (2005) states:

"An economy is a complex system consisting of myriad of agents that may be placed in three broad categories: (1) firms, (2) households, and (3) government.

Agents' interactions come under the broad umbrella of cooperation and competition while under their production and consumption activities constitute the functional fabric of the economic system.

Economic activities often span hierarchical levels of functional interdependence."

In her book, "Complexity: A Guided Tour", Melanie Mitchell (2009) describes why economies are complex systems:

Economies are complex systems in which "simple, microscopic" components consist of people (or companies) buying and selling goods, and the collective behavior is the complex, hard-to-predict behavior of markets as a whole, such as changes in the price of housing in different areas of the country or fluctuations in stock prices.
Some economists believe that economies are adaptive on both the microscopic and the macroscopic level.
At the microscopic level, individuals, companies, and markets try to increase their profitability by learning about the behavior of other individuals and companies.

This microscopic self-interest has historically been thought to push markets as a whole – on the macroscopic level – toward an equilibrium state in which the prices of goods are set so there is no way to change production or consumption patterns to make everyone better off.

In terms of profitability or consumer satisfaction, if someone is made better off, someone else will be made worse off.

The process by which markets obtains this equilibrium is called market efficiency.

The 18th century economist Adam Smith called this self-organizing behavior of markets the "invisible hand": it arises from the myriad microscopic actions of individual buyers and sellers.

Economists are interested in how markets become efficient, and conversely, what makes efficiency fail, as it does in real-world markets.

More recently, economists involved in the field of complex systems have tried to explain market behavior in terms similar to those used previously in the descriptions of other complex systems: dynamic hard-to-predict patterns in global behavior, such as patterns of market bubbles and crashes; processing of signals and information, such as the decision-making process of individual buyers and sellers, and the resulting "information processing" ability of the market as a whole to "calculate" efficient prices; and adaptation and learning, such as individual sellers adjusting their production to adapt to changes in buyers' needs, and the market as a whole adjusting to global prices.

Next Topic: An Evolutionary Theory of Trade

Raffy Saldaña
9/21/09

Notes on Complex Systems and Evolutionary Trade Models (Part 1)

.

Figure Caption: A History of Complexity Science. (Source: wikipedia.org)
.

Link to this blog post: http://raffysaldana.blogspot.com/2009/09/notes-on-complex-systems-and.html


"Science has explored the microcosmos and the macrocosmos; we have a good sense of the lay of the land. The great unexplored frontier is complexity"


-- Heinz Pagels, "The Dreams of Reason"


Recently, the Ateneo de Manila University (AdMU) Innovation Center headed by Dr. Gregory Tangonan started a complex systems study group. This semester (1st semester, school year 2009-2010) the Electonics, Communications and Computer Engineering (ECCE) Department under the School of Science and Engineering offered a graduate course on "Complex Systems" with me as one of the facilitators (together with Dr. Greg Tangonan and Dr. Nathaniel Libatique, Chair of the ECCE).

My particular interest in the topic of complex systems is on modeling and simulation. As of this writing, I am involved in a research project dealing with complex systems dynamics of evolutionary trade models and international development.

To aid my students and my research group on complex systems (and other interested readers) I am putting online some of my notes on this topic.

NOTES ON COMPLEX SYSTEMS AND EVOLUTIONARY TRADE MODELS

(Note: This is a work in progress. References will be supplied later.)

Outline:


Part I. Introduction to Complex Systems
Part II. Complex Systems and Economics
Part III. An Evolutionary Theory of Trade
Part IV. Incorporating Technological Productivity in the Evolutionary Trade Model
Part V. A Cellular Automaton Model of Evolutionary Trade
Part VI. Non-Equilibrium, Spatial Evolutionary Trade Models
Part VII. Case Study: Modeling the Impact of Changes in Transportation Cost in India
Part VIII. Case Study: A 'Policy Impact Model' for Nepal
Part IX. Agent-Based Computational Economics


Part I. Introduction to Complex Systems


• Complex systems is a scientific field which studies the common properties of systems that are considered fundamentally complex. Such systems are used to model processes in biology, economics, physics and many other fields.

• It is also called complex systems theory, complexity science, study of complex systems, sciences of complexity, non-equilibrium physics, and historical physics.

• The key problems of complex systems are difficulties with their formal modeling and simulation. From such perspective, in different research contexts complex systems are defined on the base of their different attributes.

• Since all complex systems have many interconnected components, the science of networks and network theory are important aspects of the study of complex systems. At present, the consensus related to one universal definition of complex system does not exist yet.

• Complex Systems is a new approach to science that studies how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment.

• Complex systems have multiple interacting components whose collective behavior cannot be simply inferred from the behavior of components. The recognition that understanding the parts cannot explain collective behavior has led to various new concepts and methodologies that are affecting all fields of science and engineering, and are being applied to technology, business and even social policy.


In the book "Complexity A Guided Tour", Melanie Mitchell (2009) observed that complex systems have three common properties:

1. Complex collective behavior: Complex systems consist of large networks of individual components, each typically following relatively simple rules with no central control or leader. It is the collective actions of vast numbers of components that give rise to the complex, hard-to-predict, and changing patterns of behavior.

2. Signaling and information processing: All complex systems produce and use information and signals from both their internal and external environments.


3. Adaptation: All complex systems adapt – that is, change their behavior to improve their chances of survival or success – through the learning or evolutionary processes.

From these three properties, Mitchell (2009) proposed a definition of the term complex system: a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution.

In the book "Handbook of Research on Complexity" edited by J. Barkley Rosser, Jr. (2009), six characteristics of the lowest level of complexity are mentioned:

1. Dispersed interaction among heterogeneous agents acting on each other locally in some space,

2. No global controller that can exploit all opportunities or interactions, despite the possibility of some weak global interactions,

3. Cross-cutting hierarchical organization with tangled relations,

4. Continual adaptation and learning by evolving agents,

5. Perpetual novelty as new markets, technologies, behaviors, and institutions create niches in the ecology of the system, and

6. Out-of-equilibrium dynamics with either zero or many equilibria existing, and the system unlikely to be near a global optimum.


Next Topic: Complex Systems and Economics


Raffy Saldaña
9/21/09