Describes work on an agent-based model (ABM) simulating work/information flow in a hierarchical organization that includes informal networks. Presented at SBP-BRiMS 2018, 11 July 2018. Final authenticated paper available at https://doi.org/10.1007/978-3-319-93372-6_21

1. Formal Organization,
Informal Networks, and
Work Flow: An Agent-
Based Model
Tom Briggs
SBP-BRiMS 2018 | July 11, 2018

2. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

3. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

4. Motivation
Build a reusable platform
(model) for simulating
organizational processes
Span-of-control decrees
Simulate role of informal
networks
Fun

5. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

6. The structural designers of organizations, those
who mandate reporting relationships or memo
distribution lists or access to databases, are
much like architects who try to predict where the
pedestrian traffic will be or should flow on a
university campus.
They lay their cement, install fences and other
obstacles, but inevitably the flows of people
and classes carve bare spots in the grass
where the sidewalks need to be.
Gerald Salancik, 1995

8. Formal
organizations

9. Networks

10. “Real”
organization
chart

11. Prior work
Allen & Cohen (1969) – work-
related communication in R&D labs
results from both formal structure &
social relations (e.g., “sociometric
stars”)
Katz & Tushman (1979) – R&D
laboratories, different patterns of
information flow based on type of
project and boundary-spanning
roles

12. Prior models
Ben-Arieh and Pollatscheck (2002)
mathematical model: information
overload led to individual and org
declines
Companion study looked at info
sent/received by level:
Top level 9%
Middle management 46%
Field managers 26%
Workers 20%

13. Prior models
Tsvetovat & Carley (2004) – multi-
agent, network model - boundedly
rational agents work on tasks using
ego networks to discover other
agents with needed knowledge
Lin and DeSouza (2010) – ABM,
grew networks “bottom up” based
on agent utility maximization;
suggest “preferential attachment” is
actually about maintenance

14. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

15. Model
Agent-based model of abstract
organization
Each level of employee (CEO,
managers, workers) instantiated as
software agents
At each time step agents can
receive, work on, and complete
work projects

16. Network
topography
Formal network – hierarchy, with
each team fully connected
Informal network - Erdős–Rényi
random network mechanism: each
pair of nodes is connected with
some probability P (e.g., 0.01)
Formal network always present;
informal network can be toggled
on/off & wiring probability varied

17. Org Network Model screen capture, S = 10, P = 0.0

18. Org Network Model screen capture, S = 10, P = 0.1

19. Parameters
Parameter Description Sample Values
S Supervisory span of
control
5, 10
P Probability of rewiring 0.01, 0.03
B Bottleneck – how long
is information held
before passed (if at all?)
0.1, 0.5
U Upward constraint on
information
transmission
0.90, 0.99
W Average workload in
employees’ queue –
CEO assigns work until
this average reached
100 hours, 150 hours

20. Model
action
CEO creates workjobs to maintain
average workload W across
organization (e.g., avg 150 hours in
queue)
Jobs transit through managers to
workers and managers do a
percentage of “work” on each job
Workers do a job until complete at
their level, then pass the job back
to manager if manager not too busy

21. Model
action
If manager too busy, worker looks
for an informal network tie to pass
the job to
New “owner” of job tries to pass it
up the chain
Process continues until job gets
back to CEO, where it’s recorded
as “finished”

22. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

23. Org Network Model. S=5, P=0.03, B=0.1, U=0.99

24. Org Network Model. S=5, P=0.03, B=0.1, U=0.99

25. Org Network Model. S=10, P=0.01, B=0.25, U=0.99

26. Initial results
High spans of control + more time
required by middle managers
leads to overload
If managers overloaded,
employees often doing very little
work – simply waiting for more
from managers
If employees empowered to skip
manager and/or less manager
time required, org can be efficient
even with higher span of control

27. Issues
Artifact of intrateam job passing –
need to change agent behavior
rules to make them “smarter”
CEO not sufficiently bounded in
rationality; s/he has perfect
information on every employee’s
workload
ID dependent measures of greatest
utility and run factorial experiments
(thousands of model runs)

28. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

29. Conclusions
Built initial simulation of work
“process” in a hierarchical
organization
Easy to reach “tipping point”
of inefficiency since middle
managers processing work
both ways
Computational modeling
ongoing and iterative
process…

30. Future
directions
Model extension – e.g., add
organization growth & decay
(vacancies, hires), dynamic
network tie formation using agent
cognition or personality
Calibration – more/better data to
calibrate model parameters
Validation – use experimental data
to validate model

31. All models are wrong. Some are useful.
-George Box

32. Tom Briggs
tbriggs@gmu.edu
Twitter: @twbriggs
www.twbriggs.com
Thank you
Project details:
http://bit.ly/orgnetworksABM

33. Appendix

34. Allen, T. J., & Cohen, S. I. (1969). Information Flow in Research and
Development Laboratories. Administrative Science Quarterly, 14(1), 12–19.
https://doi.org/10.2307/2391357
Axtell, R. L., & Epstein, J. M. (1999). Coordination IN Transient Social Networks:
An Agent-Based Computational Model. Retrieved from
http://www.brookings.edu/~/media/research/files/reports/1999/5/retirement%20
axtell/csed_wp01.pdf
Ben-Arieh, D., & Pollatscheck, M. A. (2002). Analysis of information flow in
hierarchical organizations. International Journal of Production Research, 40(15),
3561–3573. https://doi.org/10.1080/00207540210137611
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social
networks. Los Angeles, Calif.: Sage.
Carley, K. M. (2003). Dynamic network analysis. Citeseer. Retrieved from
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.299.8946&rep=rep1&
type=pdf
Cross, R. L., Parise, S., & Weiss, L. M. (2007). The role of networks in
organizational change. Retrieved from
http://www.mckinsey.com/insights/organization/the_role_of_networks_in_organi
zational_change
Cross, R., & Prusak, L. (2002). The People Who Make Organizations Go--or
Stop. Harvard Business Review, 80(6), 104–112.
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36. Multiplex
networks