organizational network analysis and agent-based modeling
TRANSCRIPT
ORGANIZATIONAL NETWORK ANALYSIS AND AGENT-BASED MODELING
Dr. Simone Gabbriellini GECS - University of Brescia [email protected]
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
“PEOPLE ANALYTICS” REINVENTED OPERATIONS WITH SCIENTIFIC DECISION MAKING PROCESSES
CULTUREAMP
➤ Understand the culture of your organization
➤ Track the performances of your organization
➤ Understand job movements - candidates, on boarding and exit
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
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.
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.
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.
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”?
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
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
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?
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 developmentLet 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 mentorsImprove 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 capitalSustain, mobilize and share social capital (avoid to “recreate the wheel” or share more efficiently innovative solutions)
SOME MEASURES AND THEIR APPLICATION
MEASURE MEANING INTERVENTION CAN
heterogeneity 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 groupsDevelop 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
AGENT-BASED MODELINGa brief introduction
WHAT IS AGENT-BASED MODELING?
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empirical puzzle
keep it simple…
a reasonable representation…
keep it descriptive…
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.
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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)
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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.
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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
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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
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AGENTS = OBJECTSComputationally, the nicest way
to implement an agent is with objects22
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)
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
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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)
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.”
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”
A SMALL EXAMPLE
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
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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
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?
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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%
=
=
LET’S HAVE A LOOK WITH NETLOGO
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Results averaged over 100 replications for each threshold value in the range 10 to 100, for a total of 1000 simulation runs.
% in
crem
ent
0
12,75
25,5
38,25
51
% of
link
s w
ithin
sam
e gr
oup
50
62,5
75
87,5
100
threshold
10 20 30 40 50 60 70 80 90 100
% links value blues greens reds
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
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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
