Christopher Liu Rotman /University of Toronto

1. A Simulated Ecology of Communication: Geography, Workgroups, and the Structure of Scientist Networks Christopher Liu Rotman/University of Toronto Organization Science Winter Conference February 7, 2013

2. Research Themes • Unravel the antecedents to relationships and network positions. • Ecology + Networks: • “Individuals may be universally instrumental, but their opportunities to engage in strategic relationships are unevenly distributed across ecological and socio-demographic space.” • Across multiple settings and levels. • Biotech; Senators; Regional Clusters • Causal Inference: • Quirky settings and natural experiments… • Simulation Methods

3. Motivation & Research Question • Examine the effects of micro-geography on relationships in the workplace. • Separate geographic and (confounding) organizational factors. • Examine geography in a knowledge production setting.(Festinger, et al., 1950; Marmaros & Sacerdote, 2006) • Examine alternative (i.e., mosaic vs. uniform seating) organizational policies. • Most often, it is not possible to do an “experiment”. • What might we learn about individual- vs. organizational- network topographies? Research Question: What are the consequences to changes in laboratory size and geographic distribution on relationships and networks?

4. Setting and Data (AMINO) • Setting: • Explored in one biotechnology firm. • Network data. • Complete email logs • => xcutting ties & count of open triads. • Spatial Ecology: • Geographic distance (floorplan/blueprint data). • Social (expertise) overlap. • Scientists are organized into laboratories (i.e., workgroups)

5. Spatial Ecology at AMINO Geography Xcutting Ties Brokerage (choices) Homophily Org Policy: “Like with like”. -Senior mgmt wanted “short arrows” -Individuals with longer arrows are Brokers.. Implication: Spatially diverse positions allow broader networks… N 5

6. Counterfactual Policy (Mosaic) Desired Experiment: with a Mosaic, would you see more cross-cutting ties and brokerage? N

7. Network Simulation (I) 1) Opportunity Structures • I. Collect very rich data! • Environmental Factors • Socio-demographics • (email) networks • Identify correlates of relationships • Dyad-level regression P(Randomly) P(Same building-floor) P(Same laboratory) P(Friends of Friends) 2) Preferences P(Same sex) P(Same ethnicity) P(Same cohort) P(Same education) P(Same discipline) P(Same building-floor) P(Same laboratory) Repeat 4X II. Model Network People meet People choose whether (or not) to interact Measure network variables. (Jackson and Rogers, 2007) (Mayer and Puller, 2008) 3) Network

8. Network Simulation (II) III. Tune Parameters -Choose parameters & run model (II) -ΔObs(I)– Exp(II.3) -Revise parameters to identify plausible (Px)s. 3) Network Characteristics All Ties-# All Ties-SD Local Ties Collocated/Same-Lab Ties-# Collocated/Same-Lab Ties-SD Crosscutting Ties Collocated/Diff-Lab Ties-# Collocated/Diff-Lab Ties-SD Non-Collocated/Same-Lab Ties-# Non-Collocated/Same-Lab Ties-SD Distant Ties Non-Collocated/Diff-Lab Ties-# Non-Collocated/Diff-Lab Ties-SD • IV. Simulation “Experiment” • -hold parameters constant • generate “Mosaic” • Change lab composition • - Measure changes in the simulated network. (Jackson and Rogers, 2007) (Mayer and Puller, 2008) 4) Counterfactual Experiment

9. Dyad-Level Regression

10. Model Calibration Individuals meet with a 9% probability each cycle. • Calibration is not (yet) perfect, but pretty darn close. • But the relative magnitudes of parameters seem to make sense.

11. Preferences, Homophily, Friends, Org Structure, Geography.

12. Mosaic & Lab-size Counterfactual To Be Determined… (next week)

13. Concluding Thoughts • Networks and relationships are constructed on a spatial ecological scaffold, even for strategic actors. • Simulation methods help us to do “experiments” that organizations won’t, or can’t do. • Can explore gender, ethnicity, cohort, etc. • This is a general methodology that may be applicable across multiple levels.

14. Setting and Data • Setting: • Explored in the US Senate. • Network data (DV). • Complete bill cosponsorship patterns (1979-2001). • Geography: • Spatial distance (chamber seating data). • Rearrangements over time. • Controls: • FE; Ideology; tenure;