MIS 579/677 2015/2016 Fall Computational Social Sciene by Bertan Badur Department of Management Information Systems Boğaziçi University
Outline 1. 2. 3. 4. 5. 6. What is Computational Social Science Automated Information Extraction Social Networks Social Complexity Social Modeling and Simulation Resources
1. What is Computational Social Science • Interdisiplinary examininations of the social phenomena, on many scales from individual actors to large groups, through the medium of computation. • many scales: – temporal, spatial, organizational • Compuational: – algorithms – simple distributed – data storege, transmision • Interdisiplinary – social as well as computer scinece, natural sciences
A Computational Paradigm of Society • paradigms – a useful perspectives orienting inquery • not a theory • CSS – information-processing paradigm – information - viatal role – how social systems and processes operate • explainng and understanding • social complexity
Dual Aspects of Information Processing in CSS • Substentive • Methodological
Substentive • key ingredient to explaning and understanding how society and human being operate to produce emergent complex systems • social entites use information processing – obtain information – process – behave
Methodological • Methodology: computing as a fundamental instrumentgal approach for modeling and understanding • Other methodologies – mathematical, statistical, historical • computational methods • deep and new insides
Insturnment-Enabled Science • before and after • astronomy - optical telescope, radio telescope • biology/medicine – microscope, electron microscope • nanoscinece – electron micoscope • linguistics – computers and math • physics – calculus • economics – game theory
Computing as an instrument • computing – generations – Von Neumn, parallel, cilient/server, distributed computing architectures • in deveoping social science as well as science and engineering • new instruments – new concepts – new theories – new data
Main Areas of CSS • • • Automated Information Extraction Social Networks Social Complexity Social Simulation Modeling Each has its own history, methodologies, . . • hybridization of these •
2. Automated Information Extraction (AIE) • AIE computational ideas and methodologies pertaining to the creation of scientificlly useful information - knowledge based on raws data sources • used to be done manually • other names: – automated content analysis • generate information • social, behavioral or economic patterns
AIE • Historical data sources: text data – census records, historical sources, radio broadcasts • AIE: computational algortihms • new data: images, graphs video, audio • to reduce humman affort coders - olong training and preperation • reduce humman errors • data sources exposive many internet social media • some patterns are easily detected by altortihm then coders
Examples • analysis of direct content of documnets – political orientatin of leaders or other governmental actors – computational analysis of speachs, testimony – before any legislative committees
Hybrid Examples • Networks and other structures present in raw data – organized cireme organizations and their illegal activities – computational content analysis and text mining of coart cases, other documnents describing individuals, dates, events and attributes associated with criminal individfuals • Modeling correlation across networks – internet news websides
Ohter Hybrid Examples • Simulation and AIE hybrids • calibration of model parameters – models of opinion dynamics – international trade – regional conflicts – humanitation cyrisis senarios • Extraction of geospatial, spatio-temoral data
3. Social Networks • Network: Collection of objects in which some pairs are connected by links • objects: – verticies, nodes, actors (in SNA) • connections – links, adges
A Network Example Friendship network between members of a club
Why to study networks • Individual parts or components linked by some way • Examples – Internet: collection of computers linked by communication links – social networks: people by friendship relations – businesses by raw materials – countries – trade
Why to study networks • Individual components – How a computer works – How a humen being feals or acts • Connections or interactions – how behavior is affeced from connections – Communication protocoles – Dynamics of human friendship
Examples of Networks • Technological networks: – Internet, electrical power grids, phone networkst, transport networks • Information networks: – www, e-mail, phone calls, social media, citations • Social and economic networks:
Social Networks • • Collection of social ties among friends A network of people or group of people such as firms Vertecies - People or groups Edges – connections of some kind – Friendship or acquaintances , working relations, sexual – Business relations • Sociology • Increas in complexity due to – Travel, technological advences, global communication, digital interaction • Exampe: Actor network • Wayne Zachary’s karate club - small 34 members • Facebook or MS Instant messaging – large data
Economic Netwoks • Special types of social networks • Vertecies- firms, countries, industries • Edges – trading relationships between companies or countries • Global manifacturing operations – network of suppliers • Media companies – network of advertizers • Spread of local breakdown to cascading failures or financial crisis
Ohter Methodologies • Empirical: – Study network data to find organizational principles – How do we measure and quantify networks? • Mathematical models: • Graph theory and statistical models • Models allow us to understand behaviors and distinguish surprising from expected phenomena • Algorithms for analyzing graphs • Hard computational challenges
Properties of Networks • Structure • Behavior • Dynamcs – On networks – processes on. . – Of networks – Coevolution
Structure • Hubs: vertices with unusually high degree – Observed in many networks • Algorithms for – analyzing and uderstaning • network data • Measurements of network properties • Metrics – centrality: • Quantifies how important vertices are • Degree of a node: number of edges attached to a node
Clusters or Communities in Networks • Social networks – break down into subcommunities – Knit friends or acquantions • Business relationships of companies – Clustered sets • Community detection techniwues
Behavior and Dynamics • Structure – starting point • Connectedness at the level of structure – Who is linked to whom • At the level of behavior – individual’s actions – consequenses – Outcomes of anyone
Network Dynamics – Structural Effects • How people influence each other • Taking into acount structure of the network • The underlaying mechanisms – information and – population level – Local level - network – in the structure links friends – Aligning behavior with immediate friends rather then population as a whole
4. Social Complexity • Complex systems - informally – difficult to understand – world we live getting more and more complex • many complex interractions compared to past • as science and technology progres • Simple to complex systems • Defined: • Systems with interracting many elements yet aggregate behavior can not be predictable from individual elements – from interractions of individual elements – an emergent phenomena arises • E. g. : simple population dynamics – all members are the same homogenous – complex food web – how each member interact with others
Emergence • • • large scale effects of laocal interractions lower level to higher assumptions may be simple consequences may not be obvious –suprising Micro level macro level phenomena micro – Second order emergence • Properties CAS – self-organized – order at the macro level – Chaotic behavior: small change in initial condition hase huge effects on system out – fat-tailed: extream values more then normal distibution – Adaptive interactions.
5. Social Modeling and Simulation • Models – Building simplified representations of the phenomena • social, natural, business or socio-technical • Types of models: – Verbal - Natural languages – Analog – Mathematical – equation-based • Analytical • Emprical: regression equations, neural networks – Single or structural – interraction among variables – A relation between dependent and independent variables is estimated from data • Differential / difference equations (System dynamics) • Computational method – Computer programs – Inputs (like independent variables) – Outputs (like dependent variables)
Example of a Model • Consumer behavior model: – How friends influence consumer choices of indivduals • Buy according to their preferences • what one buys influeces her friends decisions – interraction • verbal • mathematical – theoretical model – Emprical : statistical equations • estimated from real data based on questioners • simulation models of customer behavior – ABMS – interractions, learning, influence from networks
Mathematical Models • Analytical models – closed form solutions • Restrictive assumptions – Rationality of agent – rational choice theory – Representative agents – Equilibrium • Contradicts with observations – abaratory experiments about humman subjects – Observations at macro level – stylized facts • as precision get higher explanatory power lower • Relaxation of assumptions – geting a closed form solution is impossible
Example: Consumer behavcior • Consumer behavior models in economics • treat a typical consumer as a untility maximizing agent • the consumer observe prices of goods/services • derives utiity from them • perfectly rational • Mathematical tools – at minimum calculus • Interraction of consumers in a market • two or three types of consumers • equilibrium is assumed
Emprical Models • Estimation of parameters of a single or set of equations from real world data • Methods – statistics, machine learning or data mining – Regression – single equation or SEM – Nueural networks – Decisio trees • E. g. : estimate behavior of cunsumer from opinion survays • E. g. : behavior of an economy – Simultaneous equations
From Simulation to Social Simulation • Model of a system with suitable inputs and observing the corresponding outputs • Uses of simulation Axelrod(1997) – 1 -Prediction: – 2 -Performance: – 3 -Training: – 4 -Entertainment: – 5 -Education: – 6 -Proof – 7 -Understanding - Discovery:
Experiments • Experiment: – Applying some treatment to an isolated system and observing what happens • Common in natural sciences – Physics, chemistry • Not common in social sciences – isolation – Mostly in psychology, new in experimental economics • Computer simulations – chaning parameters - range – other factors randomly • if the model is a good representation of the reality – Senario or what if analysis
Simulation in Social Science • In engineering or natural science – Prediction – E. g. : predict – position of planets in the sollar system – motion of molecules – weather temperature (next day, hour) • In social science – Uderstanding social phenomena, processes or mechanizms – Proof of my claim or hypotheis – Discover some new previously unknown patterns – Policy/senario analysis
Adventages • Not restricted with unrealistic assumptions • No sofisticated mathematiical skills • Thought experiments – policy evaluation, senatio analysis • Enables to test different theories or hypothesis about a phenomena – E. g. : different consumer behavior theories
Limitations • Expresing the results – particular example • Rsults depends on – parameters – initaal conditions • Model communication – reproducibility of results – use standard packages – limitaitons • Interdiciplinary nature • Education in social science – no programming courses • May need computing power
Different Approaches • Simulation models in social science • Gilbert(2005) classification – System dynamics – Discrete event simulation – quing models – Multilevel – Microsimulation – Cellular autometa – Agent-based Simulation
Agents • Agents represents in ABMS – Individuals – consumers, producers, families – Organizations – governemts, merket makers – biological entities – animals, forest, crops • What they do – Get information from their environment or from other agents – Process information, may have limited memory forget – Communicate with one onother via messaging – Learn from others, their own experiences
Agent-Based Modeling and Simulation • Agent-based Modeling and Simulation (ABMS) – Paradigm, methodology – Modeling approach – aim – better undertand natural, social phenomena • agents – – – autonomous having properties and actions (behavior) individual heterogeneity interactive with other agent and their environments emergence of structure – macro or social levels boundadly rational - adaptation and learning behavior • ABM - Computational modeling – Constucting models – a phenomena is modeled in terms of its agents and their interactions • create, analyze, experiment with
Model Development • Model development is an iterative process • starting with problem formulation • firet simple models • get complicated • Implementation of the model – simulate the model • Varification • Validation • Analysis of the model
Other Related Modeling and Simulation Approaches • System dynamics (SD) • SD ABM : aggregate individual top- down buttom-up differential equations interacting agents • E. g. : Population dynamics • SD: a single variable for population – an equation describing its rate of channge – hard to include heterogenouty • ABM: modeling population with heterogenous agents – fertatlty, migration or death rate depends on – age, gender, income, etnicity, location
Example: Simple Populatgion Dynamics • How population of a country/region evolves over time • Assumption: Populatgion of a country increrases proportional with the current value of its population • SD – one variable representing population N(t) as a function of time – homogenous • d. N/dt = g*N – rate of change of population is proportional to curent value of N – g: yearly growth rate of population – first order homogenous differential equation
ABM model • At time 0 • create set of egents representing age, gender, education, income, etnicity, geography of population • Each agent has a type has different fertality rate • As time progress – – with a probability have a chiild may die or migrate to another country new agents may migrate to the country but deterministically age increses by say 1 year
6. Applications • Economics • Demogrphy • Political science – party competitions – voting behavior • • Socialogy / Antropology History Law Interdisiplinary – Science dynamics – socio-technical systems
Business/MIS • Business – Finance – Marketing / e-merketing – Organizational behavior – Operations management • Supply chain management / logistics – MIS • User modeling, value of information, ebusiness, e-auctions, business process modeling
Modeling Examples • Opinion dynamics – Agents have opinions -1 to +1 and degree of doubt – Interact randomly • Consumer behavior • Marketing – viral marketing WOM effects – efficiency of marketing strategies – Dynamics of markets: – U-Mart project
Modeling Examples (cont. ) • Industrial networks – – AIE, SNA, SD, ABMS Links between firms Inovation networks- biotechnology, ICT Clustering of industries • Business ecosystems • Supply chain management – SD, DES, ABMS – Effectiveness of management policy – Order fulfilment – Procter & Gamble
Business/MIS Examples • Diffusion – SD, ABMS – New product, technology, innovations • Markets – modeling software markets – versioning decisions timing of upgrading and how much and when • Financial merkets – Santa Fe Stock market – speculative behavior • Auctions – efficiency, profitability of e-auction mechanisms
Business/MIS Examples (cont. ) • Strategic management – – SD, ABMS Profitability, efficiencey of business strategies Competitive or cooperative strategies outsourcing • Organizational impact of information systems • Modeling simulation of business processes – Common with discrete event simulation but • Modeling and Simulation of Social Networks – AIA, SNA, ABMS – Behaviour in social media – Dynamics off/on social networks • How social networks evolve over time • network of networks
Business/MIS Examples (cont. ) • Industrial clusters – Similar firms in terms of what they produce (good services) – Tend to be locatyed in the same geographical regions • Software Engineering – Software upgrade quality improvement decisions in prsense of network effects • Modeling competition considering product life cycle diffusion of influences
Decision Support Systems (DSS) • simulation models can be embedded into DSS to perform – What if analysis – Sensitivity analysis – Senario analysis • User interface • Model base – – OR - optimzation – linear programming Statistical Analytical simulation
6. Resources • C. Cioffi-Revilla • Computational Social Science • Willey
Books • Gilbert, N. , Agent-Baded Models, Saga Pubnlications, 2008. • North N. , J. , Macal, C. M. , Managing Business Compoexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, Oxford University Press, 2008. • Railsback, S. , F. , Grimm, V. , Agent-Based and Individual-Baded Modeling: A Practical Introduction, Princeton University Press, 2011. • Robertson, D. , A. , Caldart, A. , . The Dynamics of Strategy: Mastering Strategic Landscapes of the Firm, Oxford University Press, 2009. –
Скачать презентацию MIS 579 677 2015 2016 Fall Computational Social Sciene by
f1c48056df8e5b4c18fad5f12ff0fea7.ppt