Complex Adaptive systems

GRS-21306

Introduction Arnold Bregt Introduction

 Shrimp farming in Vietnam Mekong Delta, Vietnam Different systems for Shrimp farming

Extensive Different systems for Shrimp farming

Improved Extensive Different systems for Shrimp farming

Intensive

Source: Michiel Oliemans Different systems for shrimp farming

Integrated Mangrove Shrimp

Source: Michiel Oliemans Two Communes in Vietnam Mekong Delta

Interviews and participatory mapping

Similar conditions different developments

Characteristics Differences

 Number of households:  1825 vs 1443  One commune -> intensive shrimps without  Bio-physical condition's trees. comparable  Other commune-> mixed  Development towards systems with mangroves intensive shrimp Reflection

 Initial conditions

 Interactions between farmers and role of hero’s

 Sequence of activities

 Adaptive capacity of all stakeholders With as a result

 Different emergent behaviour of these two communes

 Example of a complex adaptive system:  Interactions between agents  Sensitive to initial conditions  Path dependency  Open systems  Adaptive behaviour of agents

Contents Presentation

 System thinking in Wageningen

 Complex adaptive systems

 Complex adaptive systems and SDI Scientific schools

 Reductionism ( e.g. Rene Descartes)  components

 Holism (e.g. Aristotle)  system Question

 What is the science view approach in Wageningen? Thinking in systems is popular, e.g

Climate system Agricultural system

Transport system Social system System thinking

 System consists of:  Components  Relation between components  Boundary

 Systems are human constructs Framing systems

Non-linear relations

Social & Bio-physical/technical Bio-physical components components

Linear relations Framing systems

Complex Complex Systems Adaptive Systems

“Simple” Adaptive Systems Systems

Simple Systems

 Characteristics: Falling ball  A few components  Closed systems  Well defined interactions

 Behaviour:  Predictable

Adaptive systems

Driving a car  Characteristics:  Respond to changes in the environment  Feedback loops

 Behaviour:  Respond in predictable way Complex systems

Land sliding

 Characteristics:  Interaction between components non-linear  Feedback loops  Tipping points

 Behaviour:  Sudden, emerging events Complex adaptive systems

Land-use system  Characteristics:  Multi level  Open systems  Non-linear interaction  Learning  Behaviour:  “Dynamic stability”  Emergence Scientific publications (Scopus, 2-12-2015)

Complex Systems Complex Adaptive Systems # 45,666 # 2,473

“Simple” Systems Adaptive Systems # 11,846,235 # 24,276

Question

 Can you give examples of pro’s and con’s of both approaches?

Reflection

 The focus of science is on a “simple” system description of the real world

 Understandable from both a science and human point of view:  Ockham's razor heuristic  Reductionism  Bias for linear cause-effect relations

 Our mind likes to simplify (Kahneman)

Kahneman, thinking fast and slow

Reflection

 But, our real-world problems are not simple. Quite often they have:  Many components, with partly unknown relations  Adaptation takes place  History (path) is important  Open

But How?

 Complex adaptive system (CAS) view:  Theory is relative young (1990, Santa Fe)  No consensus on definitions, characteristics  Interdisciplinary field

 CAS in practice:  Last 10 years  Agent-based modelling (ABM)  Design principles Complex Adaptive System

Complex Adaptive System is a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel and constantly, and reacting to what the other agents are doing (Waldrop, 1992).

Complex adaptive system

Source: Complexity systems institute Publications

Scopus, 2016 Complex Adaptive Ssystem

Scopus, 2016 CAS Features

 Components  Complexity  Sensitive to initial conditions  Openness  Unpredictability  (Scale independence)

CAS behaviours

 Adaptability  Self organization  Non-linear behaviour  Feedback loop mechanism

Adaptability Example: Self organization Non-linear behaviour Feedback loop CAS as a research topic

CAS application in Wageningen

 CAS is the concept/ theory

 ABM (Agent Based Modelling) is the implementation)

An example application

An example of CAS in practice

H. Meyer, A. Bregt, E. Dammers, J. Edelenbos, 2014 (NL) 2015 (Eng) The Dutch South-west delta

A detailed actor and interaction analysis

Historical analysis (path dependency)

Scenario analysis

Actor interaction

Towards a Robust, Adaptive Framework

Flood defence is not a dike, but a zone

Towards a resilient and adaptive urbanized delta

Complex adaptive systems and SDI

Hypothesis:

CAS characteristics and behaviors can also be found in SDIs.

Dutch SDI

Structure of SDI of Flanders (Part of belgium) SDI as CAS (assignment)

 Please read the article from your reader “Spatial Data Infrastructures as Complex Adaptive Systems” and analyze your SDI case using the 10 features and behaviours mentioned in this article.