1. ORGANIZATIONAL NETWORK ANALYSIS AND
AGENT-BASED MODELING
Dr. Simone Gabbriellini
GECS - University of Brescia
simone.gabbriellini@unibs.it

2. OUTLINE OF THE TALK
➤ Why Organizational Network
Analysis
➤ Brief outlook
➤ What is agent-based modeling
(ABM)
➤ Brief outlook
➤ How ABM works
➤ A simple example
➤ What are silos in
organizations
➤ Insights from an ABM
➤ Conclusions

3. “PEOPLE ANALYTICS” REINVENTED OPERATIONS
WITH SCIENTIFIC DECISION MAKING PROCESSES

4. CULTUREAMP
➤ Understand the culture of
your organization
➤ Track the performances of
your organization
➤ Understand job movements -
candidates, on boarding and
exit

5. BLACKBOOKHR
➤ See how relationships and
information flow through your
organization
➤ Analyze, build and model
networks to anticipate the
effects of change.
➤ Discover the hidden
influencers in your
organization who are critical
to getting work done

6. SEEYOURNETWORK
➤ What if you could x-ray your
organization to learn how
work is really getting done?
➤ SYNAPP is an online tool for
illuminating and analyzing
how your organization really
works.

7. KEYNETIQ
➤ Visualize and understand
different types of relations
between employees directly
affecting organizational
effectiveness and agility.
➤ Implement organizational
network analysis with this
easy-to-use platform to
leverage your networks for
performance, growth and
innovation.

8. SOCILYZER
➤ Get an overview of the collaboration
within and between groups such as
geographical units, project teams, and
business units.
➤ Identify close-knit groups and
organizational silos within your
organization - and the individuals who
tie them together.
➤ Characterize your organization with
regards to being fragmented or
centralized and general level of
collaboration for knowledge sharing,
innovation, communication, and
collaboration.
➤ Find individual connectors, bridge
builders, bottlenecks, and influencers
within your organizational network.

9. ONA IN A NUTSHELL
➤ How can we retain employees and reduce voluntary turnover?
➤ How can we understand the flow of knowledge in our organizations
and facilitate the flow of new knowledge in the right direction?
➤ How can we detect bottlenecks and tensions that may jeopardize
the performances of a department?
➤ How can we avoid the “not-invented-here” syndrome, or prevent
our employees to reinvent the wheel when executing a task?
➤ How can we assess the level of reciprocity needed for cooperation
to flourish between our employees?
➤ How can we detect if our company works as a set of separated
“silos”?

10. ONA IN A NUTSHELL
➤ ONA represents a paradigm for Organizational Studies - since
’70
➤ ONA represents social structure in terms of relationships
between social actors
➤ ONA deals with the types and patterns of relationships, and
the causes and consequences of these patterns

11. ONA IN A NUTSHELL
➤ Organizational Network Analysis (ONA) is a powerful set of
theories and methods that aims at giving insights on an
organization by imagining the whole organization as a
network of people bounded by different kinds of relationships
(i.e., normative, communication, advice, trust…).
➤ Applying ONA principles to organizations aims at
understanding (Parise 2007; Borgatti and Foster 2003):
➤ knowledge creation, transfer and innovation;
➤ supporting critical bridges in networks that helps bound
the network together;
➤ retention and succession planning

12. COLLECT DATA
➤ Survey to retrieve multiple networks within the organization
➤ communication network
➤ advice network
➤ trust network
Whom do you talk to every day within the past month?
With whom do you socialize outside of work within the past month?
Whom do you turned to within the last month for answers to fairly specific or detailed questions at work?
Whom do you turned to within the last month for general guidance or referrals to other sources of
information?
Whom would you trust to keep in confidence your concerns about a work-related issue?
Whom would you recruit to support a proposal of yours that could be unpopular?

13. SOME MEASURES AND THEIR APPLICATION
MEASURE MEANING INTERVENTION
struct eq Employees with the similar patterns of connections
can behave in similar ways
Manage knowledge associated with job
succession planning
indegree on
comm net
Identifies central or critical people in the knowledge
flow of the organization.
Decrease information bottlenecks
Distribute information more effectively,
especially to people on the periphery of the
network Ensure succession and continuation
of relevant expertise
indegree on
trust net
This measure identifies the underlying corporate
culture representatives.
Identify the most trusted employees for
corporate culture development
Let trusted employees lead trust building
activities to mentor less trusted colleagues
constraint
on advice
net
Ability to collect relevant knowledge from multiple,
diverse sources; having access to diverse
perspectives improves the ability to get promotions
and be better placed in the organization flow of
knowledge.
Identify employees that need mentoring
Identify possible mentors
Improve retention and try to impede employees
turnover
constraint
on comm
net
Personal support refers to the ability to mobilize
social capital needed to cope with different tasks.
Central nodes receive information sooner than those
on a network’s periphery or access more novel
information when bridging disconnected parties.
Identify employees with high/low social capital
Sustain, mobilize and share social capital
(avoid to “recreate the wheel” or share more
efficiently innovative solutions)

14. SOME MEASURES AND THEIR APPLICATION
MEASURE MEANING INTERVENTION CAN
heterogene
ity in comm
net
Empowerment describes the extent to which an
employee is connected to colleagues outside his/her
immediate team or department; the greater your
collaboration, the greater your ability to work across
organizational silos to get tasks accomplished.
Assign brokers to ensure connections
between subgroups
Ensure knowledge connections are sufficient
both within and between subgroups
reciprocity This measure is focused on peer leadership, where
peers can speak openly and honestly with each other,
outside the structures of power and authority within
which they live and work.
Identify informal leadership groups
Develop a more strong culture within the
organization.
bet in
comm
network
This measure identifies the degree of collaborative
knowledge sharing.
Develop skills to enhance interpersonal
effectiveness . Decrease cultural or structural
barriers to collaboration
bet in
advice net
This measure identifies organizational leaders that
have access to perspectives, ideas, and networks that
are otherwise unknown to most network members.
Determine who the current brokers are now,
which can influence interventions, what these
key individuals know and also whom they
know in terms of critical relationships
bet in trust
net
the right employees to identify change initiatives
and identify the current change agents who are the
informal leaders in the organization.
Assign the right leader to solve a specific
problem or introduce a new policy in the
group. Determine who is the current informal
leaders in the network
local closet
comm net
Execution refers to the ability to get the work done.
This measure identifies how well distributed
knowledge is in an organization and also how solid the
workflow is.
Shorten the distance it takes for knowledge to
reach the entire network. Identify leverage
points in the network to improve connectivity

15. AGENT-BASED MODELING
a brief introduction

16. WHAT IS AGENT-BASED MODELING?
16
empirical puzzle
keep it simple…
a reasonable representation…
keep it descriptive…

17. DEL RIGOR EN LA CIENCIA
➤ Borges’ story imagines an empire
where the science of cartography
becomes so exact that only a map on
the same scale as the empire itself will
suffice.
➤ Jorge Luis
Borges (1946)
GENERATIVE PRINCIPLE
➤ ABM are used within the
conceptual framework of
what Epstein’s (2006) called
Generative Social Science
➤ The research question is:
“how could the
decentralized local
interactions of
heterogeneous autonomous
agents generate the given
regularity?”
➤ Formalisation:
¬S ⇐ ¬G
➤ Macy & Willer
(2002)
COMPUTATIONAL SOCIOLOGY
➤ Macy and Willer (2002) identify ABM as the
right tool for advancing sociological theory
➤ Human group processes are highly complex,
non-linear, path dependent, and self-
organizing.
➤ A bottom-up approach should be more
efficient than a top-down and aggregate one.
17

18. SO WHAT IS AN ABM?
➤ABM represent individuals, their
behaviors and their interactions
➤Agents have decision-making abilities and
an understanding of their environment
➤emergence is not a mystery: “it is precisely
the adequate description of the individual
bee that explains the hive” (Epstein
1999)
18

19. SO WHAT IS AN ABM?
➤ An ABM is a computer program:
➤ a collection of agents and their states
➤ the rules governing the interactions of
the agents
➤ the environment within which they
live.
19

20. SO WHAT IS AN ABM?
➤ ABM as a magnifying glass
(Terna 2012) that helps you to
see into the puzzle:
➤ we have some hypotheses
➤ we implement them
➤ we test them - maybe
against empirical data
➤ we can discard them and
change our framework of
assumptions
➤ or accept them because they
offer generative sufficiency
20

21. WHAT IS AN “AGENT”?
➤ An agent is a thing which
does things to things
(Kauffman)
➤ An agent is a persistent thing
which (Shalizi, 2004):
➤ has some state you find
worth representing
➤ interacts with other agents,
mutually modifying each
others’ states
21

22. AGENTS = OBJECTS
Computationally, the nicest way
to implement an agent is with objects
22

23. SO WHAT IS AN ABM?
➤ If social actors can modify their properties then:
➤ the model is dynamic: it implies that there is a before and an after
➤ there is a scheduling: who has to do what in what order under
what conditions?
➤ If you can change the values and outline a sequence of actions and
events, then you must have some rules to decide how to change
such values and define the temporal sequences
➤ The rules you pick up are your hypotheses on the social
phenomenon at stake
➤ To be more precise, these rules are the computational
translation of your hypotheses (no black boxes)

24. WHAT IS SIMULATED TIME?
➤ A schedule implies a timeline
➤ ask agents [
do something
]
increment time
➤ How can a deterministic device produce random
events?
➤ How can we use pseudo-random numbers?
➤ We also need a seed, like 123456789
24

25. ABM PRODUCES DATA
➤ Every model has a parameters
space
➤ Select a granularity
➤ Simulate each possible
combination many times
➤ Explore the parameter’s space
➤ Compare with empirical
values (eventually)

26. MEDIANEEDLE
➤ Focused on marketing
➤ Active in LA, Calif. with
clients like Adidas, Hyundai,
Metallica, RHCP…
➤ “Through ‘what-if’ scenario
planning, you can now
attribute the impact of all
brand touch points with a
very high level of accuracy
and better understand how
your customers make
decisions, before spending
marketing dollars.”

27. THINKVINE
➤ From ThinkVine White Paper:
➤ “ABM models the aggregate
phenomena rather than
modeling the underlying
data relationships”
➤ “Quantify the impact of
marketing activities and
non-marketing factors on
sales”
➤ “Forecast the likely impact
of marketing activity and
non-marketing factors on
future sales”

28. A SMALL EXAMPLE

29. ABOUT SILOS IN ORGANIZATIONS
➤ Organizations don’t establish silos with the goal of destroying trust,
stifling communication and fostering complacency.
➤ They do it to allocate resources efficiently.
➤ The existing literature on interorganizational networks strongly
suggests a tendency among network members to perpetually favor a
homophilic pattern in which partner “similarity breeds connection”
➤ When silos are present:
➤ trust is destroyed
➤ communications are stymied
➤ the organization grows complacent
➤ culture development is hampered
29

30. ABOUT SILOS IN ORGANIZATIONS: HOW TO SPOT THEM
➤ Silos are tightly vertically integrated teams in which
individuals tend to work closely together but have limited
interactions with other parts of the organization (other silos)
➤ Silos are associated with poor communication and duplicative
problem solving that can make organizations less efficient and
adaptable.
➤ Markers for informational silos:
➤ Low complexity: few connections between groups
(clusters/community)
➤ High internal density: groups are tightly connected
internally

31. ABOUT SILOS IN ORGANIZATIONS: THE CAUSES
➤ CAUSES (Tett 2015; Bevc et al. 2014):
➤ Enterprises are organized around functional departments where information management systems
sometimes are unable to freely communicate with other information management systems because:
➤ Lack of direction from the top regarding regular meetings and formal communication gives
tacit permission for employees to form silos.
➤ Managers control the flow of information and:
➤ either have an incentive to maintain the status quo
➤ or additional costs associated with integrating the information systems may not justify a
change.
➤ SOLUTION: break the silos!
➤ Managers of successful firms spend a lot of their time trying to ensure that information flows freely
between departments to ensure that all aspects of the company are functioning effectively.
➤ Contemporary management views suggest that the silo mentality mindset must be broken in order
for employees to remain motivated and be happy to come to work.
➤ Efficient companies promote the sharing of information in an attempt to let the combination of
groups function as a team.
➤ Question: would silos exist without managers?
31

32. OUR MENTAL EXPERIMENT WITH ABM
➤ a simple organization with 3 groups:
➤ red
➤ green
➤ blue
➤ each group has 50 employees
➤ each employee share information with
everyone
➤ RULE: each employee wants that at
least a % of neighbors belong to his/her
group
➤ if rule is met:
➤ employee is happy
➤ otherwise:
➤ employee replaces one neighbor
threshold = 60%
=
=

33. LET’S HAVE A LOOK WITH NETLOGO
33

34. Results averaged over 100 replications for each threshold value
in the range 10 to 100, for a total of 1000 simulation runs.
%increment
0
12,75
25,5
38,25
51
%oflinkswithinsamegroup
50
62,5
75
87,5
100
threshold
10 20 30 40 50 60 70 80 90 100
% links value blues greens reds

35. CONCLUSIONS
➤ ABM are a robust tool to investigare the “perverse
effects' of social action” (Boudon 1984), i.e. the fact
that our actions, when aggregated, might have
unintended consequences
➤ As you have seen, the aggregation of (even benevolent)
individual actions might lead to suboptimal outcomes
➤ The concept of “tipping point” emerges (Schelling
1971)
➤ ABM are useful to aid our intuition and conduct better
mental experiments
35

36. REFERENCES
➤ Bevc, Retrum, and Varda, “New Perspectives on the “Silo Effect”: Initial Comparisons of Network Structures Across Public Health
Collaboratives”, American Journal of Public Health, 2015, 105(Suppl 2): S230–S235
➤ Borgatti and Foster, “The network paradigm in organizational research: a review and typology”, Journal of Management, 2003, 29,
991-1013
➤ Boudon, “The Unintended Consequences of Social Action”, Social Forces, 1984, 63(2),
➤ Epstein, “Agent-based computational models and generative social science”, Complexity, 4(5):41-60
➤ Railsback and Grimm, Agent-Based and Individual-Based Modeling. A practical introduction, Princeton, 2012
➤ Macy and Willer, “From factors to actors”, Annual Review of Sociology, 2002, 28, 143-166
➤ Hedstrom and Manzo, “Recent Trends in Agent-based Computational Research”, Sociological Methods Research, 2015, 44(2):179-185
➤ McPherson, Smith-Lovin and Cook, “Birds of a feather: homophily in social networks”, Annual Review of Sociology, 27, 415-444
➤ Merrill, Caldwell, Rockoff, Gebbie, Carley, and Bakken, “Findings from an Organizational Network Analysis to Support Local Public
Health Management”, Journal of Urban Health, 2008, 85(4), 572–584
➤ Parise, “Knowledge management and human resource development: an application in social network analysis methods”, Advances in
Developing Human Resources, 2007, 9, 359-383
➤ Schelling, “Dynamic models of segregation”, Journal of Mathematical Sociology, 1971, 1, 143-186
➤ Shalizi, C.R: (2004), “Methods and Techniques of Complex Systems Science: An Overview”, arXiv:nlin/0307015v4
➤ Terna, Pietro. “Learning Agents and Decisions: New Perspectives” Informatica e diritto 22.1 (2013): 115-129
➤ Tett, The silo effect. The Peril of Expertise and the Promise of Breaking Down Barriers, Simon&Schuster, 2015

37. THANK YOU FOR YOUR ATTENTION
simone.gabbriellini@unibs.it