Ethnographic Methods & Computational Social Science

1. Ethnographic Methods & Computational Social Science Lee Hoffer, Ph.D., MPH Dept. of Anthropology National Institutes of Health, National Institute on Drug Abuse: DA09232, DA06016, & DA019476 National Science Foundation, Social, Behavioral & Economic Sciences, Cultural Anthropology: 0951501

2. For my talk… What is Ethnography? What is Computational Social Science (agent-based modeling)? Present the opportunities & challenges in merging these methods to research drug addiction & illegal drug markets • Focus on the method, not the subject matter • Provide background

3. The Illicit Drug Market Simulation Project(IDMS) Projects (3) combining ethnographic data & agent-based modeling (ABM) to inform our understanding of illegal drug markets (DA019476, DA025163, BCS-0951501) Common Elements: Use ethnographic data in ABM development Build models that inform theory & policy

4. The IDMS Project (1992-2001) University of Colorado, Denver, CO. • NIH, NIDA F31 Pre-doctoral fellowship – DA06016 (Junkie Business: Sept.1997 – Feb. 1999) (2005-2008) Washington University School of Medicine, St. Louis, MO. • NIH, NIDA R21 Evaluating the Social Structure of a Local Heroin Market– DA019476 (Hoffer, PI) (2008-2014) Case Western Reserve University, Dept. of Anthro., Cleveland, OH. • NSF, Merging Agent-based Modeling Techniques and Ethnography: A New Analytic Tool for Studying Illicit Drug Use Behaviors, Markets and Economies – BCS-0724320 (Hoffer, PI) Cleveland, OH • NIH, NIDA RO1 Researching the Social Dynamics of a Local Methamphetamine Market – DA025163 (Hoffer, PI) Akron, OH

5. Ethnographic Research Methods Overview The hallmark of cultural anthropology Open ended interviewing & participant-observation techniques (i.e., fieldwork) Requires rapport with participants Collecting & verifying – accounts, beliefs, and observed behaviors over time (analytically inductive)

6. Ethnographic Research Methods Overview • Specific strengths… • Provides a narrative in context (“lived experience”) • Presents highly detailed / personal accounts • Offers explanations (not associations) • Best method for certain populations or topics Theory development (uses modified “Grounded Theory” approach)

7. Ethnographic Research Methods Overview • Specific limitations… • Non-experimental science, no tests / quantification • Makes assumptions about social processes • Can use “culture” to explain outcomes (black box) • Relativistic

8. IDMS Project 1: Proof of Concept study • The Heroin Market Model • (Backstory, Data, Unfinished business)

9. The Ethnographic Data Junkie Business was an 18-month case-study of a heroin dealing network (Denver, CO., 1997-1999) The study described: • Operational activities of drug dealers • How the heroin market operated • The adaptation of that market (natural experiment*) • * During the course of this research the heroin market was “closed” to make way for MLB (Thomson Wadsworth, 2006)

10. The Ethnographic Data Open-air Market Private Market • Immigrant seller • Imported labor, transitory • Street-based sales • Did not use drugs • Received commission • Local Junkie Broker • The open market “residents” • Connected buyers & sellers • Required “payment” (tax) • Private Dealer • Outside open market • Addicted to heroin • Needed introduction • Lots of “rules”     Three Options for Customers

11. Implications • “Closing” illicit drug markets can increase the efficiency of drug distribution & promote negative health outcomes • But this was not the focus of my work

12. Health Consequences? Colorado CEWG Data:Heroin ED Mentions* Denver CEWG Data: Heroin TX Admissions* * % of Total Admissions *per 100,000 Population

13. Implications • The market adapted because brokers play an essential role in its long-term functionality • Could not be removed (i.e., arrest was ineffective) • Maintained consistent motives (i.e., the heroin tax was their primary source for drugs) • Provided link between sellers & customers The “Waterbed” theory of illegal drug markets

14. Epilogue to Junkie Business The end was only the beginning… • How could I extend my findings? …make them useful in other drug market contexts (more generalizable) …concerns of “representation,” quantification, and culture • How could I make sense of self-organizing social systems? …market adaptation without leadership …spontaneous, unplanned, and undirected organization …individual’s pursued their own goals but structure emerged (Thomson Wadsworth, 2006)

15. Complex Systems (Computational Social Science) • Patterns of behavior involve (non-linear) dynamic interactions between heterogeneous agents • Systems cannot be explained by aggregating individual behavior • Systems “emerge” from: 1) individuals interacting with other individuals & 2) adapting, interacting, & changing their environment Agent-based modeling can reproduce emergence by “growing” (simulating) systems using agents

16. A “Complex” History GameTheory System Dynamics Complexity Science Micro Simulation 1970’s Queuing models Cellular Automata Artificial Intelligence 1980’s Agent-Based Modeling 1990’s Neural Networks 2000’s Artificial Life Genetic Algorithms New Science of Social Networks Multi-Agent Models Learning / Evolutionary models Computational Social Science

17. Agent-Based Modeling (ABM) • “Agents” are computer programs that can… • Perceive their environment & other agents • Act and interact w/ other agents • Change & remember states & actions (non-Markov process) • Execute rules, heuristics, & strategies • Represent: individuals, households, groups, firms, nations... (ABM’s can incorporate heterogeneity, feedback, randomness, etc.) • A form of computational modeling that uses agents to express processes thought to exist in the social world • ABM’s are simplified descriptive models • (Macy & Willer, 2002; Sawyer, 2004; Gilbert, 2008; Wooldridge & Jennings, 1995)

18. Agent-Based Modeling (ABM) • To program ABM’s the researcher needs to program: • Agents • An environment • Rules However… • When running an ABM the outcome is unpredictable, i.e., we do not know what the aggregated product of these interactions will be • (Macy & Willer, 2002; Sawyer, 2004; Gilbert, 2008; Wooldridge & Jennings, 1995)

19. Agent-Based Modeling: Types • Abstract • Model basic social processes implied in a variety of social domains, non-empirical • Segregation (Schelling) • Cooperation (Axelrod) • Artificial life (Epstein & Axtell) • Middle Range • Models abstract enough to have some generalizable outcomes, still not case specific • Segregation (Macy) • Social Networks (Barabási, Watts) • Facsimile • Models reproducing the features of a specific case, geography, or history • Archeological models (Kohler) • Bali water temples (Lansing) • Drug trends (Agar) (Gilbert, 2008) • Most ABMs are “abstract”– intend to inform, test, model theory not policy • “Data” in support of model assumptions often a secondary consideration

20. Agent-Based Modeling: T. Schelling • The Environment: two colors of homes randomly distributed on a grid • The Rules: 1) Each home evaluates the color of the homes surrounding it and…2) moves if the number of similar color homes surrounding it is “unacceptable” • An acceptance level of 37% (3 of 8 similar neighbors) produces clusters of homes the same color • Segregation can occur when agents are tolerant of others not like them Based on individual (i.e., independent) behaviors of agents a counterintuitive & unpredicted macro-pattern emerged (W.W. Norton & Co. 1978)

21. Each agent requires 6 of 8 neighbors to be similar (.75%) = .9% total similar Each agent requires 1 of 8 neighbors to be similar (.12%) = .5% total similar Each agent requires 3 of 8 neighbors to be similar (.37%) = .8% total similar Agent-Based Modeling: T. Schelling “Tolerant” agents “Tolerant” agents “Intolerant” agents “Diverse” environment Segregation Segregation (Wilensky, U. 1999. NetLogo. http://ccl.northwestern.edu/netlogo/)

22. Agent-Based Modeling: Types • Abstract • Model basic social processes implied in a variety of social domains, non-empirical. • Segregation (Schelling) • Cooperation (Axelrod) • Artificial life (Epstein & Axtell) • Middle Range • Models abstract enough to have some generalizable outcomes, still not case specific. • Segregation (Macy) • Social Networks (Barabási, Watts) • Facsimile • Models reproducing the features of a specific case, geography, or history. • Archeological models (Kohler) • Bali water temples (Lansing) • Drug trends (Agar) • Discovering & researching “emergence” is critical • Ethnographic data on illegal drug markets (IDMS)

23. The IDMS Project: Ethnography & ABM

24. IDMS Project 1: Proof of Concept study • The Heroin Market Model • (Can we do this?)

25. The Heroin Market Model: “Toy Model” (Wilensky, U. 1999. NetLogo. http://ccl.northwestern.edu/netlogo/)

26. The Heroin Market Model: Customer assumptions Assumption 1: Agents have an understanding of “tolerance” & are tolerant to the drug Assumption 2: Agents seek & use heroin to remain “well” (withdrawal avoidance) Assumption 3: Agents seek & use heroin to get “high” (euphoria seeking) Assumption 4: To acquire heroin, agents must purchase it in a market with cash Assumption 5: Agents can use a broker, street seller, or dealer to acquire the drug • Lots of things missing… only a partial (restricted) representation

27. ABM Opportunity: Agent design “Biological” Motives Avoiding withdrawal & getting high Individual Resources Using real money to purchase drugs Agent Behavior Social Environment Purchasing drugs in a market

28. The Heroin Market Model: Overview The Objective: Model the operation of the Larimer area heroin market The Environment: Open-air and private market areas The Agent behaviors: • Customers: use heroin & have addictions, purchase heroin, remember dealer locations • Street dealer: sell heroin within market area, are “visible” to other agents, work specific hours (i.e., market “closes”) • Private dealer: sell heroin outside market area, only visible to customers who “know” them, meet customers through brokers • Brokers: make purchases for customers, know street dealer locations, can know private dealers, can introduce customers to private dealers • Homeless:wander the market (i.e., noise) • Police: harass and remove (i.e., arrest) agents that possess heroin

29. The Heroin Market Model: Agent Specifications • Both simple & more complex agents • Most behaviors were “generic”… – e.g., buying / selling heroin, interactions Street dealer Customer Anylogic™ 5.0, XJ Technologies

30. The Heroin Market Model: Parameters • Parameters are input values (e.g., drug prices) & probabilities (e.g., probability of knowing a private dealer) • Can be variable, fixed or inactivated (i.e., 0% or 100%) • Known, unknown, and unexpected (come back to this)

31. The Heroin Market Model: Screen shot Anylogic™ 5.0, XJ Technologies

32. Market Transaction Totals by Seller Customers: N=200; Street Dealers: 20; Private Dealers: 25; Brokers: N=50; Homeless: N=100 (20 simulation runs)

33. ABM Opportunity: Experimentation Sim 1: agents + environment Outcome(s) of interest (Change) Sim 2: agents + environment Outcome(s) of interest Sim 3: agents + environment Outcome(s) of interest Sim 4: agents + environment Outcome(s) of interest Unlimited amounts of data…

34. Market Transaction Totals “bust scenario” N=30 police agents added, 24 hrs. Customers: N=200; Street Dealers: 20; Private Dealers: 25; Brokers: N=50; Homeless: N=100 (20 simulation runs)

35. ABM Opportunity: Model resolution The “range” of ABMs Population / Market Social group (network) Agent Because you start with the agent…

36. Customer Agent “Addiction patterns” Human Model Assumed to be equivalent to animal model Animal Model Generated by pressing a lever to self-administer drug Social Environment Acquisition of drugs through the drug market (relationships / interactions) What are the patterns? ChenS.A., et all., Neuropsychopharmacology (2006), n=8

37. Customer Agent “Addiction patterns”

38. IDMS Project 1: Proof of Concept study • The Heroin Market Model • (Can we do this?) • YES!

39. IDMS Projects 2 & 3: Overview • First generation of ethnographic projects designed for ABM… • How can we do this better?

40. Where does ABM “fit” into ethnography? Using ABM to represent findings (simulating the system researched) • Content overly developed • Lots of interconnected parts • ABMs are best organized as “partial representation”

41. Where does ABM “fit” into ethnography? Using ABM to develop & refine questions, themes, and findings (part of analysis) • This setting is too dynamic • Data may be underdeveloped • Less confidence in findings • ABM modeling takes time & collaboration efforts

42. Where does ABM “fit” into ethnography? Constructing a narrative in support of our interpretation (connecting data, theory, and result) • Data rich • A focused & narrowed topic • Simulation to enhance not replace the narrative • Greater emphasis on resolutions & “negative case” analysis Using ABM to test components of our mental models, assumptions, & explanations

43. Where does ABM “fit” into ethnography? Assumption 1: Agents have an understanding of “tolerance” & are tolerant to the drug Assumption 2: Agents seek & use heroin to remain “well” (withdrawal avoidance) Assumption 3: Agents seek & use heroin to get “high” (euphoria seeking) Assumption 4: To acquire heroin, agents must purchase it in a market with cash Assumption 5: Agents can use a broker, street seller, or dealer to acquire the drug • Assumptions become explicit in the computer code

44. How do we set parameters? • Types of parameters: • Known (e.g., heroin price) • Unknown, but potentially measurable (e.g., the heroin “tax”) – here we can estimate values until data are collected • “Known, but not measurable” (e.g., decision to use a broker) • Some parameters represent processes situated within a specific social context • Ethnography focused on delineating algorithms vs. collecting numbers

45. How do we set parameters? “On a methodological level, one begins to worry about numbers in ways not captured in the usual quantitative/qualitative debates. Complex models require numbers. But it is not so much a question of how to measure phenomena; instead, it is a question of how to express qualities learned through anthropological research, using functions instead of words as the language for that expression.” (Agar 2001: 364)

46. How do we validate our models? • ABMs are descriptive and not predictive models! • What kind of validity is important, & for what type of model? (underdeveloped topic) • Can we evaluate the “product” of ABMs against other data sources? (“gold standards” very problematic in illegal drug market research) • Using ethnographic data to inform ABM assumptions & processes can results in a high degree of internal validity

47. Other lingering challenges • Using ABMs in computational social science requires trans-disciplinary collaboration (computer programmer, mathematician, social scientist) • Most ABMs are only informed by “subject-matter” experts or literature review (data collection protocols are needed) • Caution is needed when interpreting results (ABMs are “incomplete” representations and descriptive)

48. Conclusion • Computational Social Science is gaining popularity & acceptance (Lots of opportunity, moving from theoretical to policy modeling) • Ethnographic research (focused on social processes) can make an important contribution • More research combining ethnography & Computational Social Science is required

49. Acknowledgments Georgiy BobashevRTI International, Durham, NC Robert (Joey) Morris RTI International, Durham, NC Michael AgarRedfish Group, Santa Fe, NM Joshua Thorp Redfish Group, Santa Fe, NM Burchan BayazitWashington University, St. Louis, MO Allison Schlosser CWRU Research Team, graduate student Sarah Koopman-Gonzalez CWRU Research Team, graduate student Karen Flynn CWRU Research Team Pat White CWRU Research Team Website: case.edu/artsci/anth/Hoffer.html (model doc. Available) National Institute on Drug Abuse: DA06016, DA019476 National Science Foundation: BCS-0951501 THANK YOU!

50. IDMS Projects 2 & 3: Overview The Models: • Drug preference switching model – Modeling polydrug use, drug acquisition costs, addiction, & police pressure to understand non-urban trends in drug use(In progress) • Heroin price (tax) model – Modeling communication “costs” about heroin dealsas redistribution within heroin markets (In progress)