COMPLEXSOCIAL

SYSTEMSLECTURE 5, 15.9.2015

INTRODUCTION TO COMPUTATIONAL SOCIAL SCIENCE (CSS01)

LAURI ELORANTA

• LECTURE 1: Introduction to Computational Social Science [DONE]

• Tuesday 01.09. 16:00 – 18:00, U35, Seminar room114

• LECTURE 2: Basics of Computation and Modeling [DONE]

• Wednesday 02.09. 16:00 – 18:00, U35, Seminar room 113

• LECTURE 3: Big Data and Information Extraction [DONE]

• Monday 07.09. 16:00 – 18:00, U35, Seminar room 114

• LECTURE 4: Network Analysis [DONE]

• Monday 14.09. 16:00 – 18:00, U35, Seminar room 114

• LECTURE 5: Complex Systems [TODAY]

• Tuesday 15.09. 16:00 – 18:00, U35, Seminar room 114

• LECTURE 6: Simulation in Social Science

• Wednesday 16.09. 16:00 – 18:00, U35, Seminar room 113

• LECTURE 7: Ethical and Legal issues in CSS

• Monday 21.09. 16:00 – 18:00, U35, Seminar room 114

• LECTURE 8: Summary

• Tuesday 22.09. 17:00 – 19:00, U35, Seminar room 114

LECTURESSCHEDULE

• PART 1: Social Complexity

• PART 2: Complexity Theory

• PART 3: Complex Adaptive Systems

LECTURE 5OVERVIEW

SOCIAL COMPLEXITYAS A HISTORICAL CONCEPT

• From historical perspective social complexity can be understood as

the extent to which a society is governed by something else than a kin-

based relations -> via more complex social systems.

• Based on relations of authority

• E.g hunter-gatherer groups can be seen as simple social systems, where

as modern democracies can be seen as very complex

• Can be traced historically to the evolution of first agricultural societies

SOCIAL COMPLEXITY AS A HISTORICAL CONCEPT

(Cioffi-Revilla, 2014.)

• Service scale is a measure of (historical) social complexity where one is

able to compare the ordinal relation of social complexity of two different

social systems:

Band < Tribe < Chiefdom < State < Empire

• Phase transitions between entities on the service scale

• E.g from Chiefdom to State

• Can be used in mapping the historical origins of societies and

sociogenesis

• West Asia, East Asia, South America, Mesoamerica…

SERVICE SCALEELMAN R. SERVICE

(Cioffi-Revilla, 2014.)

• Globalization, gave rise to significant rise to level of social complexity

• Globalization understood as a rapid increase in size (network diameter)

and connectivity of world system of policies

• Endogenous globalization: within given polity region

• Exogenous globalization: between polity regions

• From West Asia & Europe to East Asia

• From Europe to America

• Highly increasing amount of complexity in today’s societies

• Economy & Trade

• Politics

• Information Networks

GLOBALIZATION AND COMPLEXITY

(Cioffi-Revilla, 2014.)

• Three types of systems

1. Natural systems

2. Human systems

3. Artificial systems

• Artificial systems (or artifacts) exist because they have a function: they

serve as adaptive buffers between humans and nature

• Humans pursue the strategy of building artifacts to achieve goals

• Two kinds of artificial systems working in synergy

• Tanglible (e.g. roads, buildings)

• Intanglibe ( e.g. organisations, social structures)

SIMON’S THEORY OF ARTIFACTS AND SOCIAL COMPLEXITY

(Cioffi-Revilla, 2014.)

• Goal seeking behavior: humans are not passive agents, but instead

striving actively for certain goals

• Basic goals: Survival & improvement

• Adaptation: goal seeking requires adaptation to environment

• Artifacts: adaptive buffers between humans and environments

• Polity: complexity is manifested in the way society is governed

• Ordinal scale of social complexity: different types of society can be

compared based on their complexity

BASIC FEATURES OF SOCIAL COMPLEXITY

(Cioffi-Revilla, 2014.)

• Bounded rationality: goal seeking agents are typically not perfectly

rational: i.e. decisions are made based on bounded rationality. Social

agents have imperfect and faulty information and biases.

• Emergence (of phenomena): macroscopic phenomena arise from

microscopic behaviours (emergence will be elaborated more in relation

to complexity theory slides below).

• Near-decomposability:

• Social systems are formed of modular hierarchies of parts and parts

of parts. Entities that are closer to each other typically have

stronger/more connections.

• Typical example is an organization with business divisions or business

functions.

STRUCTURAL FEATURES OF SOCIAL COMPLEXITY

(Cioffi-Revilla, 2014.)

• Social complexity is a latent variable, meaning that one cannot measure

it directly

• All measures are indirect “proxy” indicators

• I.e. size of social organisations, size of cities etc.

1. Qualitative social complexity indicators

2. Quantitative social complexity indicators

SOCIAL COMPLEXITY MEASURES

(Cioffi-Revilla, 2014.)

• Structural: built environment, structures for public and collective use

• Pictorial: Imagenary and visual representations o culture, history, politics,

economy…

• Artifactual: artifacts requiring social complexity (an organisation) to make

• Epigraphic: written manifestations of social structures

• Forensic: remains of a society have typically indicators social complexity

• Locational: selection of the location of the society might provide some

clues towards social complexity

QUALITATIVE INDICATORS OF SOCIAL COMPLEXITY

(Cioffi-Revilla, 2014.)

• 1. Formal measures, based on mathematical approached

• Graph based metrics and information theory metrics

• Undirected clustering coefficient

• Shannon’s entropy

• 2. Substantive measures, based on specific social, economic, political

and cultural variables

• Peregrine-Ember-Ember ordinal Guttman scale of social complexity (1.

Ceramic production -> 15. Money of any kind)

• Human Development Index (UN, aggregate socioeconomic conditions)

• Lexical measure of social complexity (minimal description)

QUANTITATIVE INDICATORS OF SOCIAL COMPLEXITY

(Cioffi-Revilla, 2014.)

COMPLEXITY THEORY

• Complexity is a debated concept: 1. what can be considered

complex? 2. how to model and research complexity?

• No agreed universal definition of complexity or complex system

• Parts versus the whole (micro vs. macro): i.e. can you research

complexity by researching the parts of the complex system only?

• Structure versus agency: i.e. can you research complexity by

researching the structure only, and what is the relationship between

structure and agency?

• Deep ontological and epistemological debates/problems when

discussing about modeling complexity or simulating complexity

• Positivism/Empiricism vs. critical realism vs. complex realism

• Some authors don’t consider big parts of agent based simulation of

complex systems to be science at all.

COMPLEXITY IS COMPLEX

(Byrne & Callaghan, 2014)

• “Complex systems present problems both in mathematical modelling

and philosophical foundations. The study of complex systems represents

a new approach to science that investigates how relationships between

parts give rise to the collective behaviors of a system and how the

system interacts and forms relationships with its environment.”

(Wikipedia 2015, Complex Systems)

• A system is a set of interacting or interdependent components forming

an integrated whole. Every system is delineated by its spatial and

temporal boundaries, surrounded and influenced by its environment,

described by its structure and purpose and expressed in its

functioning. (Wikipedia 2015, System)

COMPLEXITY IS A PROPERTY OF SYSTEMS

A SYSTEM

System

Border

Environment

• “… a simple system is one to which a notion of state can be

assigned once and for all, or more generally, one in which

Aristotelian causal categories can be independently segregated

from one another. Any system for which such a description cannot

be provided I will call complex. Thus, in a complex system, the causal

categories become intertwined in such a way that no dualistic language

of state plus dynamic laws can completely describe it. Complex systems

must then process mathematical images different from, and irreducible

to, the generalized dynamic systems which have been considered

universal.”

DEFINING COMPLEXITYROBERT ROSEN

(Byrne & Callaghan, 2014)

• Linear (Newtonian) systems state can be described as a function of its

values & parameters. Linear systems can also be described with

universal laws.

• “A nonlinear system, in contrast to a linear system, is a system which

does not satisfy the superposition principle – meaning that the output of

a nonlinear system is not directly proportional to the input.” (Wikipedia

2015, nonlinear system)

COMPLEX SYSTEMS ARE NONLINEAR

(Image is public domain. Low pressure system over Iceland by Nasa.)

• Complex systems show emergent behavior, meaning that they may

give rise to phenomena and structures that are strikingly different

and unforeseen from the underlying structures and attributes.

• Abrupt system transitions, multiplicity of states, pattern formation,

unpredictable evolution in space and time.

• Emergence is closely related to chaos theory and nonlinearity

• Organization can emerge from chaos

• Emergence can be seen happening based on the interactions between

the parts of the systems, or as more holistic interaction between the

whole system and its parts (in essence micro vs. macro)

COMPLEX SYSTEMS & EMERGENCE

(Byrne & Callaghan, 2014)

(Image is public domain. Snow flakes by Wilson Bentley.)

• The Game of Life, is a cellular automaton developed by the British mathematician John H. Conway in 1970.

• Simple rules that create emergent phenomena:

1. Any live cell with fewer than two live neighbours dies, as if caused by under-population.

2. Any live cell with two or three live neighbours lives on to the next generation.

3. Any live cell with more than three live neighbours dies, as if by overcrowding.

4. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.

(Wikipedia 2015, Conway's_Game_of_Life)

http://pmav.eu/stuff/javascript-game-of-life-v3.1.1/

GAME OF LIFE AND EMERGENCE

• Complex systems far from equilibric: their state may change radically

• This does not mean, that complex systems can’t be stable for long

periods of time

• Far from equilibric emphasizes the potential or radical change from

stable equilibric state

• This can be understood also as a adaptation capability: open complex

system can adapt radically to its environment

• Complex systems state space can oscillate towards many attractors

FAR FROM EQUILIBRICSYSTEMS

(Byrne & Callaghan, 2014)

• “An autopoietic machine is a machine organized (defined as unity) as a

network of processes of production (transformation and destruction) of

components of which: (i) through their interactions and transformations

continuously regenerate and realize the network of processes (relations)

that produced them; and (ii) constitute it (the machine) as concrete unity

in space in which they (the components) exist by specifying the

topological domain of its realization as such network. “ (Maturana and

Varela 1980)

• In another words, autopoietic systems are self producing: they self-

generate the processes and interactions that generate the whole system.

• Autopoietic systems do not stay the same, but constantly evolve and

change.

AUTOPOIESIS

• In a critical view to complexity one could distinguish restricted and

general complexity

• Restricted complexity: Views that the complexity can be researched

by deconstructing its inner parts and researching the interaction of the

parts

• Agent based modeling and simulation as prime examples

• General complexity: Views (holistically) that the whole is greater than

the parts and complexity cannot be researched by researching the

structure (parts) of the whole if this is possible, then it is not real

complexity or one is actually recreating real complex system

• Naturally, not all agree on this distinction

RESTRICTED AND GENERAL COMPLEXITY

(Byrne & Callaghan, 2014)

COMPLEX ADAPTIVE SYSTEMS

• Complex Social Systems are complex systems or social life. Typically

complex social systems are modeled as Complex Adaptive Systems,

underlining their capability of change and adapt in relation to its

environment.

• Basically means viewing any social organization as an complex system

that has goals, that learns and that adapts.

COMPLEX ADAPTIVESYSTEMS

• “Complex adaptive systems are a 'complex macroscopic collection' of

relatively 'similar and partially connected micro-structures' – formed in

order to adapt to the changing environment, and increase its

survivability as a macro-structure.

They are complex in that they are dynamic networks of interactions, and

their relationships are not aggregations of the individual static entities.

They are adaptive in that the individual and collective behavior mutate

and self-organize corresponding to the change-initiating micro-event or

collection of events.” (Wikipedia 2015, Complex adaptive systems)

COMPLEX ADAPTIVE SYSTEMS

• Levin (2002) defines complex adaptive systems based on three

properties:

• 1. Diversity and individuality of its components

• 2. Localized interactions among those components

• 3. An autonomous process that uses the outcomes of those

interactions to select a subset of those components for replication or

enhancement (= evolution)

PROPERTIES OF COMPLEX ADAPTIVE SYSTEMS

• Holland (2006) defines the following major features for complex adaptive

systems (cas):

• Parallelism: big number of agents interacting at the same time, in

parallel

• Conditional action: actions of the agents usually depend on the

signals they receive

• Modularity: interaction patterns and behavior based on modularity,

micro level rules, that together are able to react on macro level

• Adaptation and evolution: the agents in cas change over time: they

learn which rules work and which do not and they discover new rules

FEATURES OF COMPLEX ADAPTIVE SYSTEM

• Key research approach is related to modeling complex adaptive systems

in various ways

• Agent based modeling/simulation

• Equation/Formula based modeling/simulation

• Statistical modeling based on aggregate data

• Social Network Analysis

• Case analysis method (also QCA)

• Byrne & Callaghan (2014) underline the importance of holism

(quantitative + qualitative) in research and connecting the models and

simulations to real world (data) in some way: otherwise your simulations

might be naïve fantasies of reality.

RESEARCH METHODS FOR COMPLEX SYSTEMS

(Cioffi-Revilla 2014, Byrne & Callaghan 2014.)

• Model is a formal and purposeful representation and abstraction of

reality

• Scientific Modeling is a scientific activity, the aim of which is to make a particular

part or feature of the world easier to understand, define, quantify, visualize, or

simulate by referencing it to existing and usually commonly accepted knowledge.

It requires selecting and identifying relevant aspects of a situation in the real

world and then using different types of models for different aims, such as

conceptual models to better understand, operational models to operationalize,

mathematical models to quantify, and graphical models to visualize the subject.

(Wikipedia 2015, Scientific Modeling)

• Reality Abstraction Model of the Phenomena

MODEL

1. Models of Phenomena: model based on real world phenomena (e.g.

how ants collect food)

2. Models of Data: modeling based on raw data (e.g. plotting)

3. Models of Theory: model is the structural and formal presentation of

a textual theory

• Different Modeling Perspectives (Ontological)

• Physical models (e.g. miniature buildings)

• Fictional models (e.g. Bohr model of atom)

• Mathematical models: set-theory models, equations..

• Descriptions

• Mixed models

• A good summary on scientific modeling:

• http://plato.stanford.edu/entries/models-science/

MODELS AS REPRESENTATIONS

(Stanford Encyclopedia 2015.)

• Deep epistemological and philosophy of science related questions,

which are not unproblematic

• What is the true relationship between the model and reality?

• What can be actually researched with models?

• What questions the models are actually able to answer?

• “A fact from them [simulation] is, at best, the outcome of a computer

simulation; it is rarely a fact about the world” (Smith 1995, referred in

Lansing 2003)

• Modeling takes also a certain stance on the philosophy of science,

leaning towards empiricism & positivism, or at least critical realism.

MODELING IS PROBLEMATIC

• A play in a theatre ends, and people start to clap. Initially some stand up

to cheer for the actors, but some decide initially to sit down. What will

happen next? Will a standing ovation occur, and how could this be

determined?

• How to model a standing ovation?

• How information spreads between the agents?

• How actions of agents are timed?

• What is the behavior of the agents?

EXAMPLE: THE STANDING OVATION MODEL

• The standing ovation problem modeled as complex adaptive system?

• 1. Watch the video of Scott E. Page explaining the standing ovation

problem in general terms:

https://www.youtube.com/watch?v=3wfLoeBjwBA

• 2. Read the original research article to get the feel how the actual

research was done and how the article was structured:

http://vserver1.cscs.lsa.umich.edu/~spage/ONLINECOURSE/R2Standin

gOvation.MillerPage.pdf

• What is the relationship between the standing ovation model and reality?

Can the model tell something about reality or does it only tell something

about the model?

LECTURE ASSIGNMENT 1

• Read Warren Weawer’s article on science and

complexity.

• "Science and Complexity", American Scientist, 36: 536 (1948).

• Based upon material presented in Chapter 1' "The Scientists Speak,"

Boni & Gaer Inc.,1947. All rights reserved.

• What does he mean by organized and disorganized complexity?

LECTURE ASSIGNMENT 2

• Why all hipsters look the same?

• 1. Read this story to get an overview: http://www.washingtonpost.com/news/storyline/wp/2014/11/11/the-mathematician-who-proved-why-hipsters-all-look-alike/

• 2. Read the research article to get the details: http://arxiv.org/pdf/1410.8001v1.pdfTouboul, J. (2014). The hipster effect: When anticonformists all look the same. arXiv preprint arXiv:1410.8001.

• What modeling methods are used?

• How is the research paper structured?

• What problems there are in the model?

• Do you find the model relevant?

LECTURE ASSIGNMENT 3

• Miller, J. H., & Page, S. E. (2004). The standing ovation problem. Complexity, 9(5), 8-16.

• Lansing, J. S. (2003). Complex adaptive systems. Annual review of anthropology, 183-204.

• Geli-Mann, M. (1994). Complex adaptive systems. Complexity: Metaphors, models and reality, 17-45.

• Innes, J. E., & Booher, D. E. (1999). Consensus building and complex adaptive systems: A framework for evaluating collaborative planning. Journal of the American Planning Association, 65(4), 412-423.

• Holland, J. H. (2006). Studying complex adaptive systems. Journal of Systems Science and Complexity, 19(1), 1-8.

• Levin, S. (2003). Complex adaptive systems: exploring the known, the unknown and the unknowable. Bulletin of the American Mathematical Society, 40(1), 3-19.

• Tan, J., Wen, H. J., & Awad, N. (2005). Health care and services delivery systems as complex adaptive systems. Communications of the ACM, 48(5), 36-44.

LECTURE 5 READING

• Cioffi-Revilla, C. 2014. Introduction to Computational Social Science. Springer-Verlag, London

• Page, S. E.; Miller, J. H. 2007. Complex Adaptive Systems. PrincetonUniversity Press, Princeton.

• Byrne, D.; Callaghan, G. 2014. Complexity Theory and The Social Sciences. Routledge, New York.

• Maturana, H. R.; Varela, F. J. 1980. Autopoiesis and Cognition: The Realization of the Living (Boston Studies in the Philosophy of Science, Vol. 42). Kluwer Group, Dordrecht.

• Holland, J. H. (2006). Studying complex adaptive systems. Journal of Systems Science and Complexity, 19(1), 1-8.

• Levin, S. (2003). Complex adaptive systems: exploring the known, the unknown and the unknowable. Bulletin of the American Mathematical Society, 40(1), 3-19.

• Stanford Encyclopedia of Philosophy, 2012. Models in Science. http://plato.stanford.edu/entries/models-science/

REFERENCES

Thank You!

Questions and comments?

twitter: @laurieloranta