Perturbations of interdependent (science) teams reveal perfect and dysfunctional teams
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Lawless B. Perturbations of interdependent (science) teams reveal perfect and dysfunctional teams. Oral presentation at 2017 SciTS Conference. Clearwater Beach, FL. Jun 14, 2017. Team Formation And Cohesion. Online at: http://www.scienceofteamscience.org/2017-agenda.
Cooke & Hilton (2015) reported interdependence
statistically associated with the best science teams,
but not theoretically, with an unknown effect on team
size. With our goal a metrics of team performance
derived from a theory of interdependence, we have
established that a team’s performance cannot be
determined by its membership (Lawless, 2017); that
scientifically evaluating teams with self-reports or
interviews cannot perfect or fix dysfunctional teams,
or predict team performance (e.g., winners of sport
competitions, political contests, or jury decisions);
but that competitions (perturbations) among teams
improves science and social welfare.
Team size is a fundamental barrier to a complete
theory of teams: How to aggregate the contributions
of team members? Cummings (2015) found that the
worst performing scientific teams were the most
interdisciplinary, but by removing interdisciplinarity,
that the best scientific teams were highly
interdependent, implying that forced interdisciplinarity
adds redundancy to teams, impeding teams by
reducing interdependence. From a traditional social
science perspective, Centola & Macy (2007, p. 716)
speculated that redundancy improves a team’s
efficiency. However, Lawless (2017) found support
for Cummings by comparing oil firms as teams
operating under a dictatorship, where they were highly
redundant, versus oil firms as teams operating in a
democracy, where they were highly interdependent;
e.g., compare Sinopec’s 124.6 employees/M BBL
of oil to Exxon’s production with 15.5 to see that
redundancy creates inefficiency and serves as a source
of corruption.
With Fourier pairs from Cohen (1995), our theory of
interdependence for two factors or competing teams is:
[A,B]=iC → σAσB ≥ 1/2 (1)
From Equation (1), the exact knowledge of the standard
deviation for factor A (σA) precludes simultaneously
the exact knowledge of factor B, leading to several
matches of theory and observation; e.g., from Arrow
(1951/1963), aggregating preferences of three or
more individuals is impossible without a vote (viz., the
majority rule in a democracy) or a unilateral decision
(e.g., a dictatorship). Thus, a team of interdependent
members does not aggregate summarily. If aggregation
occurs with degrees of freedom, then, without
adjustment, ∑nindividuals = dof. But if the perfect team acts
as a single unit, then ∑dofteam = 1.
Assuming the best available individuals fill the roles
of a team and log (dofteam) equals to its entropy gives
log(dof(perfect team)) ≤ log(dof(dysfunctional team)) (2)
With this model from theory, team fit is crucial;
interdisciplinarity is functional only if it improves team
fitness; as a team’s dof increase, due to redundancy,
role conflict, lack of communication, etc., team
performance deteriorates, allowing us to revise Eq. (1)
to the standard deviation of entropy produced by team
structure (least entropy production, or LEP) times that
for performance (maximum entropy production, or
MEP): σLEP σMEP ≥ 1/2 (3)
From Eq. (3), as σLEP→ 0, in the limit σMEP → ∞; thus,
the best performing (science) teams expend the
least effort on team structure, generating MEP for a
team’s mission. In contrast, when a team becomes
dysfunctional, illuminated by a perturbation, say a team
divorce, Eq. (3) is reversed: as σLEP → ∞, in the limit
σMEP → 0; i.e., a dysfunctional team expends entropy
to tear its structure apart. Concluding, with further research, ceteris paribus, we expect to find that larger
team structures generate more entropy than smaller
ones (i.e., more arrangements are possible), requiring
more energy (revenue); that the perfect team operates
emotionally in a state similar to a ground state, the
dysfunctional team at a perturbed, excited state; and
that the perfect team’s generation of information
to itself and outsiders is subadditive (Von Neumann
information) while a dysfunctional team’s information
generation is additive (Shannon information), the two
forming a metric for a team’s performance, whether the
teammates are humans, machines or robots, a key step
for the science teams of tomorrow.
Language(s):
English
Type of Publication:
Oral presentation
Keywords:
scits 2017 conference, presentation, interdependent teams, science, team formation
Addresses these goal(s):
- Enhance team performance, interactions, and attitudes
- Conduct research on/evaluate team science
Resource created by Jane Hwang on 10/5/2017 3:52:06 PM.