Team Resilience in Complex and Turbulent Environments: The Effect of Size and Density of Social Interactions Page: 4
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et al.  find that the quality of emotional expression
characterizing interactions is a source of team resilience.
Morgan et al.  show four main resilient characteristics
of elite sport teams: group structure, mastery approaches,
social capital, and collective efficacy.
Organizational resilience is also related to the organiza-
tional structure. Decentralized decision-making structures,
network approaches, and team-based organizations show
higher adaptive capacity than hierarchy and centralized
authority [4, 66, 67].
3. The Model
We consider a team of M individuals collectively perform-
ing a task in a complex and turbulent environment. The
team decision-making process is modelled referring to
the model first developed by Carbone and Giannoccaro
 and then by De Vincenzo et al. . Here, the team
is engaged in solving a combinatorial decision-making
problem, consisting in identifying the combination of mul-
tiple and interdependent decisions, yielding to the highest
payoff for the organization. For example, consider a sourc-
ing team which is assigned with the task to procure raw
materials for a company. This task can be conceptualized
in terms of interdependent decisions, such as looking for
potential suppliers, choosing the supplier, and preparing
the contract. The team should make the decisions so as
assuring a high performance to the company. Each specific
combination of choices is assigned with a fitness value,
measuring how good is that combination for the company,
using the NK fitness landscape, where N stands for
multiple decisions (assumed to be binary) and K for
the interdependence among them. This problem space
(referred as fitness landscape) consists therefore of 2N
possible combinations of choices on activities. Specifically,
the NK fitness landscape is generated by following a stochas-
tic procedure, which permits to assign the payoff, P(d), to
each combination of choices on decisions d=(d, d2...,
dN). The payoff value, P(d), is computed by the following
P(d)=V+iN [V(d)-V], (1)
V(d)= N ,((2)
and V is the statistical average of V(d). C is the contribu-
tion that the decision j leads to the total system payoff.
The latter is drawn at random for a uniform distribution
[0, 1]. Notice that, as effect of the interdependencies
among decisions, C depends not only on how the deci-
sion j is resolved but also on the interdependent decisions.
K controls the complexity of the landscape. The higher K,
the more complex the landscape is. Solving a NK Kauf-
mann combinatorial problem, that is, finding the optimum
of the NK landscape, is classified for K > 2 as a NP-
complete problem  (for details about the landscape
generation see [25, 28, 29, 37-41, 69]).
3.1. Team Collective Decision-Making Process. We consider
that any individual k formulates his/her own opinion 6k (
, , ...,NaN) concerning the preferred combination of
choices on decisions, so as to optimize a personal payoff
function (perceived payoff), which depends on the level of
knowledge of the individual about the problem. The latter
is coded by means of the probability p that each single agent
knows the contribution C(a) to the total fitness. Being D the
matrix whose element DkJ takes the value of 1 with probabil-
ity p and 0 with probability (1 - p). The perceived fitness of
the agent k is so defined:
Vkl(Yk) - I1jkjC(ok)
However, we also consider that the natural tendency of
individuals to avoid conflicts and be in agreement with the
people they interact with pushes them to modify their own
opinions, taking into account the opinions of the other team
The dynamic process is modelled by means of a
continuous-time Markov process, whose transition rates are
defined to capture these two drivers of individual behavior
in teams [24, 25].
We consider that each agent k is characterized by the
state vector 6k = (6k, 6, ... ,o6N) with k = 1, 2, ... , M,-6k
with j= 1, 2, ... , N is a binary variable modeling the opinion
of the individual k on the decision d,. It is a binary variable
Each individual interacts with the other team members
expressing its own opinion on each decision and listening
to the opinion of the other team members.
We describe the social interactions occurring among
the individuals by means of a multiplex network, where
each layer corresponds to the specific decision d,. On each
layer, the nodes are the individuals and the links are the
social interactions occurring among the team members
and concerning that specific decision. The multiplex net-
work is described by a N-block adjacency matrix A (see
The state of the whole system is then defined by the vec-
tor s =(s1,s2, ... ,s;, ..., sMxN) ( ) ---.. 1 6 ,6 --..6
, 2... , 6M, , ... oNM). The dynamics of the system opinions
(spins) is governed by means of a continuous-time Markov
chain where the probability P(s, t), which at time t, the state
vector takes the value s out of 2MxN possible states, satisfies
the following master equation:
dP(s, t)- ws I
dst) =- s; - s'; P(s;, t) + w s - s)P s t),
where s; = (s1, s2,... , S;, ... , SMxN) and s= (s1 s2,... , - s;,
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Giannoccaro, Ilaria; Massari, Giovanni F. & Carbone, Giuseppe. Team Resilience in Complex and Turbulent Environments: The Effect of Size and Density of Social Interactions, article, July 24, 2018; Cairo, Egypt. (https://digital.library.unt.edu/ark:/67531/metadc1234365/m1/4/: accessed March 20, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; crediting UNT College of Arts and Sciences.