Unravelling the complexity of teams via
a thermodynamics perspective
W.F. (Bill) Lawless
Paine College,
Departments of Math &
Psychology
Augusta, GA, USA
wlawless@paine.edu
Complexity in Business Conference
(CCB) 2014, Washington, DC
Ira S. Moskowitz & Ranjeev Mittu,
Information Management &
Decision Architectures Branch,
Code 5580, Naval Research
Laboratory, Washington, DC 20375;
ira.moskowitz@nrl.navy.mil,
ranjeev.mittu@nrl.navy.mil
1
Summary: A countable Hilbert space
-> Thermodynamic metric of teams
• Measuring social reality -> incomplete assessments (Lawless et al., 2013); claims about nature are infused with illusions
(Adelson, 2000) or mistakes (Graziano, 2014) that +/- impair team thermodynamic effectiveness & efficiency of RE&SR.
• Once a reality claim becomes obvious to others as an illusion or mistake, counterclaims generate social dynamics.
• Universally, groups form for and against claims (e.g., mergers; green energy; climate warming), implying +/- N -> +/power; interdependence causes trends as N moves back & forth to form or break limit cycles.
• Boundaries are maintained with MT or suppression, -> +/- evolution.
• In interdependence states, predictions are ineffective; solved i -> effective predictions; suppressed i -> mistakes &
illusions, impairing RE&SR (e.g., forced consensus by gangs, EU, China, Cuba; Lawless et al., 2014)
• Unlike rational models, measurement collapses interdependence (superposition) -> incompleteness => quantum
models (interference) + biological models (tracking N)
2
The function of groups?
• The ultimate function of human decision-making is unknown, but has been attributed to reason (Aristotle), religion,
exploration, reducing suffering and death & improving the human condition (Sen, 2000). We attribute it to the
evolution of society by the multi-tasking (MT) that converts a group into a team to increase its productivity &
autonomy (Lawless et al., 2014).
• But rational models based on methodological individualism have failed (Ahdieh, 2009; see also Helbing,
2013); e.g., game theory (Von Neumann & Morgenstern, 1953; Axelrod (1984, pp. 7-8): “[competition] leads to a
poor outcome for all".); information theory (IT); social learning theory (SLT)
• How to model? From earlier work, the groups that increase productivity and autonomy are associated with
free movement of labor & money (free markets), public science, and checks & balances -> social turmoil that,
in a constrained democracy (Republics, rule of law, limited autonomy, hierarchy), + social evolution (e.g., civil rights):
• A higher UN HDI (human development index) is associated with indices for more freedom (r=.74, p<.05), less
inequality (r=.60, p<.05; i.e., Gini), and a cleaner environment (r=.72, p<.01).
• More freedom is associated with a lower interest rate on 10-year sovereign bonds (r=.58, p<.05).
• As a nation's competitiveness increases, its 10-year sovereign bond interest rate significantly decreases (r=-0.85,
p<.01).
• Hypothesis: If the function of a group is to help its members survive, team survival improves "when each team
[competes] for more resources ... " (Manhattan project, WP, 2014, 7/26, p. E12) in a public struggle, as with free
markets; when checks and balances are present; and when public feedback reduces mistakes (e.g., Justice Holmes,
1919; Justice Ginsburg, 2011).
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My history with interdependence: In 1983, I blew the whistle on
DOE for mis-managing its military nuclear wastes in practices
only it approved
Interdependence:
Public awareness in 1985
stopped DOE’s use of cardboard
boxes; public awareness in
2000 accelerated the closure of
its old burial ground
Lawless, W. F. (1985). "Problems with military nuclear
wastes." Bulletin of the Atomic Scientists 41(10): 38-42.
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My PhD Question: What causes organizations with world class scientists &
engineers to agree to make tragic mistakes? (e.g., DOE, USN, USAF, AF, NASA)
One answer: DOE’s forced consensus -> collapse of competitive
interdependence for solving idp’s (see also authoritarians: gangs; monopolies;
command decision economies, etc.; in Lawless et al., 2014).
• Can this be modeled computationally?
• Not with traditional, convergence models. Traditional organization (Pfeffer & Fong, 2005) & team theories of
interdependence have failed (Bell et al., 2012), either as too subjective (e.g., medical research; Ulrich, 2002), or
too behavioral (e.g., network science: Barabási, 2012)
• In a series of AAAI symposia at Stanford on our models of interdependence for computational teams, we
have argued that subjective interpretations and real behaviors are both necessary (Lawless et al., 2011);
also, interdependence (groupiness) => information theory (Conant, 1976), uncertainty (Lawless et al., 2010), &
data fusion (aggregation):
• 2015: “Foundations of autonomy” (http://www.aaai.org/Symposia/Spring/sss15.php; https://
sites.google.com/site/foundationsofautonomyaaais2015/)
• 2014: “The intersection of robust intelligence and trust in autonomous systems”, http://www.aaai.org/
Symposia/Spring/sss14symposia.php#ss05
• 2013: “Trust & autonomous systems” (AAAI Spring Symposium at Stanford: http://www.aaai.org/
Symposia/Spring/sss13symposia.php#ss07)
Complexity in Business Conference
(CCB) 2014, Washington, DC
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Prior Results #1: Institutionalized & forced consensus (like DOE pre-1985 or EU pre-1995) collapses
interdependence -> more mistakes and fewer solutions to idp’s (Lawless et al., 2008)
Prior Results #2: Today, however, under competing supervision by multiple agencies (including NAS), decisions
made by DOE at SRS are significantly improved (Lawless et al., 2014)
• Group Mistakes:
• Department of Energy: Despite claims to the US Congress it was protecting the environment before 1983, DOE
contaminated its nuclear weapons complex (1940-1985) with a cleanup costs in hundreds of billions (Lawless et al., 2008).
• USS Naval incidents:
• Emotional convergence caused the USS Vincennes Aegis in 1988 to shoot down an Iranian airbus;
• Command convergence suppressed warnings by the submarine crew to its Commander against a rapid ascent, causing
the USS Greenville tragedy in 2001 that sunk a Japanese tour boat; and
• Relaxation convergence after a long deployment led the submarine crew of the USS Hartford to relax and even fall
asleep while at their duty stations and the Commander and navigator to be absent from the Bridge, causing the collision
with the USS New Orleans in 2009.
• Predator:
• In 2011, despite a warning by observers to not fire that did not get transmitted and then was retracted, friendly fire by a
USAF Predator killed a Marine and Navy medic in a fire fight in Afghanistan.
• Machine Mistakes:
• Air France 477 crash. From the investigation of the 2009 crash, initiated by faulty sensors, its automated flight director
system gave erroneous instructions to the pilots who stalled the aircraft, killing all aboard.
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Foundations #1: Multi-tasking => bistable perspectives
• Groups function to multi-task (Ambrose, 2001), like a firm, baseball team, or team of decision makers (e.g., multirobot teams)
• (Bohr, 1955) Human action & observation are complementary => information incompleteness
• Individual humans multi-task poorly (Wickens, 1992); MT by a team => interdependence among independent
agents (Smith & Tushman, 2005); MT => multiple perspectives held simultaneously by a team's mind
• Schwartz (2014): Fusion cannot be fixed; Llinas’ (2014) “call for action among the fusion, cognitive, decisionmaking, and computer-science communities to muster a cooperative initiative to examine and develop [the] …
metrics involved in measuring and evaluating process interdependencies … [otherwise, the design of] modern
decision support systems … will remain disconnected and suboptimal going forward.”
• Action and observation derive from independent brain systems (Rees et al., 1997)
• But observation and behavior interact interdependently => bistable information (Lawless et al., 2010)
• Bistable interpretations => illusions/mistakes -> + U (Adelson, 2000; Graziano, 2014); social challenges
reduce U, producing social dynamics disables teams solving wdp’s, but empower a team of R&D
• A natural bias: Bistable phenomena cannot be held in awareness simultaneously (Caccioppo et el., 1996):
tradeoffs => the focus of attention shifts between bistable perceptions; + focus -> + non-foci uncertainty
• Interdependence = a superposition of Gaussian states: |Ψ> = a |action> + b |observation>, where |a|**2 =
probability of being in a state when measured (e.g., training decreases skill uncertainty while increasing
observation uncertainty; Lawless et al., 2000) -> Measurement uncertainty is natural
Bi-stable Image
=> multiple
interpretations
Stable Image
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Foundations #2: Team Dynamics
• Survival depends on team transactions to collect free energy to explore & to offset entropy losses from
disorganization
• What of a team's structure? -∆A=∆E-T∆S, if dS = deS+diS (Nicolas & Prigogine, 1992), new resources =>
reduced dS -> + A -> metrics (LEP???)
• Group transactions recur when benefits outweigh costs (Coase, 1960)
• Laws, regulations and businesses attempt to maximize benefits from transactions
• Horizontal integrations (mergers) attempt to reduce market costs (e.g., the EU; MS & Nokia)
• But many organizational processes reduce available energy by increasing entropy; e.g., mistakes
• Mistakes recur when bistability is suppressed or not harnessed (e.g., dm by forced consensus-seeking;
authoritarians; gangs; e.g., DOE; NSA; China -> metrics (MEP???)
• Despite more funds, DOE Hanford's consensus-seeking CAB has slowed nuclear waste cleanup vs DOE's
Savannah River Site's majority ruled CAB which has accelerated cleanup
• As social uncertainty increases, bistable interpretations (a mixture of reality and illusions) spontaneously arise among
agents
• Illusions increase oscillations = social dynamics (e.g., mistakes; volatile stock markets; increased mobile phone
churn; increased divorce rates).
• Reactance against illusions dampens oscillations; e.g., science; the bottom line in business; courtroom and political
debates
• Reactance -> can seed new organizations (IBM gave birth to Apple, MS)
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A simple computational example
of a team
• Kenny et al., 1998 (interdependence ≠ iid):
• Conant, 1976 (information theory = iid):
• Information Theory: interdependence is a constraint; but
IT fails in bistable environments (illusions, U, tradeoffs
between skills & resources)
• Cohen, 1996 (Fourier pairs for bistability):
• SDT: σtσf >1/2 => complex tradeoffs
From Conant, 1976, SMC
• Explains why convergence processes can fail with
bistable interpretations (e.g., + U -> + incompleteness)
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Information Theory: From “slaves are the
team” to “individuals are the team?”
Information Theory1
dof
Entropy
X1 X2 X3
1
0.00
X1 X2 X3
2
0.918
X1 X2 X3
2+
1.00
X1 X2 X3
3
1.584
But IT ≠> MT in teams, LEP, MEP
Hypothesis: Purpose of LEP = MT
• The best team structure is the
arrangement that generates least S.
• Dysfunction (all independent) -> +
structural entropy,2 waste, group
collapse, or ineffective mergers
• interdependence -> -dof,3 thus,
S=f(log W) ≈ log dof => best team
structures are at LEP (ground state)
Hypothesis: Purpose of MEP=RE&SR
• Problem solving => + MEP
• functional => +MEP
• Competition => +MEP -> + stability
1IT
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from Balch, T. (1997), Social Entropy: A new metric for
learning multi-robot teams, Proc. 10th Int’l FLAIRS Conf.;
2ADHD from Sato et al. (2013), Neuroimage, 77: 44-51.
3Kenney et al., 1998:
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MEP examples => dark social networks derived from MT
or suppression
• At $2.13 million per full-time equivalent, Apple towers over
other tech companies (from ISI Group; Fortune, 10/31/2013).
• Microsoft made about $841,900/employee; IBM about
$213,000/employee (Wolfram, 2014). Dell made about
$517,000/employee (CSI Market, 7/30/2013).
• In search, Google made about $300,000/employee (US News
& World Report, 7/17/2014).
• MEP => DM in a democracy versus DM in an autocracy, gang
or dictatorship
• Natural Boundary Maintenance: Proprietary darkness
(DM by Firms; Boards); under competition -> + RE&SR
-> +SWB, + social evolution & - mistakes (Apple; USA)
• Widespread forced darkness: DM controlled by gangs
or dictators -> -SWB, -social evolution & + mistakes
(MS-13; Cuba; N. Korea, Russia; China)
Presse et al., 2013, RMP
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Foundations #3: SDT = Fourier pairs => Bistability: 1. Measurement of
social oscillators affects the objects measured => [H]UP; 2. FPs => COI
• Humans do not react solely to sensory information, but also Gestalt groupings & experiences
(Adelson, 2000), => U from observational interpretations > U from sensory signals (SDT)
• When eigenvalues for the matrices of two teams agree, their matrices commute: [A,B] = 0
• Convergence reduces usable information (e.g., confirmation bias & rational decisions =>
incompleteness); e.g., the freedom index of nations is inversely related to the number of
deaths from earthquakes (r=-.42, p>.05).
• 6-sigma firms are less innovative (Christensen, 2011, "How pursuit of profit kills innovation ...")
• Disagreement => no commutation: [A,B]=iC; (C is a "gap" in R): Fourier pairs => σtσf >1/2
• Competition exploits U to drive social dynamics, e.g., challenges, tradeoffs, & compromises
• Skills convergence in a Gaussian distribution => tradeoffs in Fourier pairs; e.g., motor &
observation skills. From Cohen (1995): in signal detection theory, a “narrow waveform yields a
wide spectrum, a wide waveform yields a narrow spectrum and that both the time waveform
and frequency spectrum cannot be made arbitrarily small simultaneously.” (p. 45)
Color
Red
Green
Black
Function
.33
1
− t2
2
.94
−t 2
8
2.66
f1 (t ) = e
f (t ) = e
f
−2 t 2
f1 (t ) = e
Fourier Transform
F1 (s) =
2π
e
2
F(s) = 2π e
1
− s2
8
1
− s2
2
F2 (s) = 2 2π e−2 s
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F
f F
5.01
1.67
2.51
2.36
1.25
3.33
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Foundations #4: FP model of MT in a team of individuals
under bistability ≠ intuitive
• Each agent represents a Gaussian distribution with
interdependent Fourier pairs: as MT skills +
(distribution narrows), observational skills (distribution broadens)
Teams: SIT = 2.00
SMT = 0.00 => LEP
• The diamond represent a multitasking team.
• Bidirectional information flows between multi-tasking
agents
Team => MT ->
MEP
• 1. As uncertainty in MT skills decrease on a leg,
among team members, in the limit [A,B]skills -> 0
(e.g., 6 sigma processes) -> [A,B]observation -> iC ≠
innovation (Christensen, 2011).
• 2. Excess ∆A:
Information Out
• The glue for a team’s structure => boundary
maintenance, LEP -> team vector
Information In
• A successful team structure (S->0 = LEP) has
excess ∆A (Coase, 1937) for MEP => RE&SR
Evidence: Considering all teams in major league baseball, as fielding percentage
improved, indicating skills, so too did a team’s winning percentage (r=.41, p<.05).
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Evidence of Bistability: MT (interdependence) -> +
observational uncertainty
• Hypothesis #1: As uncertainty in an agent's observations decrease, uncertainty in its actions increase
• Individuals:
• Tversky (Shafir & LeBoeff, 2002): the justification for an action is unrelated to the action performed
• Lawless et al. (2000): no association between book knowledge of air combat skills and performance in combat
• Self-esteem (Baumeister et al., 2005); Kelley's (1992) preferences v choices under interdependence
• Despite the supposed adherence by women who reported taking HIV prevention pills 95% of the time (=> drug failed), the measure of
effective drug levels in their blood near the time of infection was <26% (=> drug succeeded; Science, 2012).
• Zell, E. (2013), “Self-Insight of Ability”, found the mean correlation of r=0.29 between 22 scales for self-evaluation and performance,
SD + 0.11, indicating a mean variance of less than 10%.
• Hypothesis #2: As uncertainty in a team's skills decrease, uncertainty in its observations increase
• Firms:
• Bloom et al. (2009): estimations by managers of their firm’s performance is unrelated to actual firm performance
• K => S = 0; but, intergenerational K in small businesses doesn’t pass to 3rd generation (Stalk & Foley, 2012, HBR)
• Coca-Cola's marketing fiasco in 1985 to replace traditional coke with a reformulated "new" coke based on 200,000 consumer taste tests
(http://www.coca-colacompany.com/history/the-real-story-of-new-coke)
• Uncertainty in the observations of better run organizations is noise or darkness (e.g., MS-13; Apple; in Lawless et al., 2011)
• Zipf (1949): Habits minimize wasted effort; business culture is a habit of firms, teams and organizations (Lawless et al., 2014).
• Example of teamwork: Many great golfers use swing coaches
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Application #1: Using computational bistability
to solve idp’s & prevent or correct mistakes
• MEP = RE&SR from cultural noise that democracy & science
& R&D generate for society and businesses to exploit
• Adam Smith’s “invisible hand”
• [A,B] -> COI = Fourier pairs: σaσb > 1/2
• |ψ> = a|interpretation> + b|opposite>
• Justifies R&D and scientific journals
• Smallman (2012): used visual displays of consensus
agreement vs disagreement to break apart confirmation bias
• Consensus: [A,B]=0 => solves wdp’s, stability but increases
mistakes
|opposite>
• Disagreement: [A,B]=iC => solves idp’s, prevents mistakes,
increases social evolution
• Competition reduces mistakes (orthogonal interpretations;
e.g., Portfolio theory, Markowitz, 1952)
• The result is a computational approach to indirect control of
hybrid systems; but it is insufficient (i.e., not RI)
|Defense Attorneys>
Socially
acceptable
decisions
|Merger Prey>
|interpretation>
|Prosecutors>
|Merger Predators>
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Cultural noise (social MEP) identifies problems; solutions
come from RE&SR: debate, free markets, public science (e.g.,
politics, courtrooms) = Social computing -> social evolution
• Let two speakers each represent opposing groups, with an audience of
neutrals in front of both.
• To model debate, the two different views do not commute; i.e., [A,B]=iC;
where i represents phase space, and C a gap in the Knowledge, K, of
Reality, R, where K => H-> 0 in the limit
• RE&SR (Hayek, 1945, The uses of social knowledge)
• Seek competition, not consensus (Hackman, 2011); i.e., successful debate
increases social welfare (the winning organization and society gain ∆A)
• Hypothesis: Despite open conflict (+S) caused by illusions and mistakes,
democracies solve idp’s better than authoritarian regimes:
• DOE’s consensus-seeking Hanford Board’s nuclear waste cleanup ->
more spending & more mistakes than majority-rule SRS Board
• China’s groundwater, air, and food supply; DOE’s widespread
radioactive waste contamination (political, governmental
suppression ≠ MEP)
Neutral deciders
• EU’s White Paper, 2001
• Cultural noise can also derive from the failure to adopt unified social
standards (Lawless et al., 2014, forthcoming, JET)
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RE&SR (MEP) -> Universal social solutions as neutrals
move into & out of interdependent states; e.g., NYT, 2010
• Strong believers have a single focus (Anderson,
2004) -> stable believers
• Neutrals hold both views in mind, simultaneously
(e.g., F. Scott Fitzgerald,1936) -> unstable believers
• A loss of neutrals -> conflict (Kirk, 2003)
• A superposition of neutrals (QM; 90 deg rotations)
=> non-commutative, orthogonal operators
(Gershenfeld, 2000), thus, don't sample!
• |ψ> = a |conservatives> + b |progressives>
• Limit Cycles => 90 deg rotations (Beninca et al.,
2009), -> oscillations (biology)
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Constructing social reality: Model of a business team as an
Engine (survival => Eavailable > Sproduction)
Imaginary (Bistable beliefs) Axis
Oscillatory:
Rational Behavior:
[A,B]pure skills
[A,B]pure observations -> iC;
e.g., NRC & DOE
• Ground State:
[A,B]MT skills -> 0;
Team = MT
=> MEP
• [A,B]MT observations -> iC
Real Behavioral Axis
• MT Structure: S -> 0
(LEP)
1. Bistable social model (competing
views)
I out
I in
2. Explains why teams need coaches
• [A,B]=iC
Interdependence
!
• |Ψindividual> = a|0>+b|1>
• |Ψdyad> = a|00>+b|11>
• Fourier pairs: σaσb > 1/2
4. Successful teams have (dark) LEP
structures (additive vectors = vortex)
-> MEP
3. Competition Centers => Social
computing => + SWB, + Evolution
(Lawless et al., 2013), Structure & Dynamics
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Conclusions: interdependence is a social resource for
business teams & individuals under uncertainty:
• 1. For an individual, computational robust intelligent MT is unlikely, precluding
productivity & autonomy; these require the self-organized MT of a team;
• 2. For a business team, bias reduces thermodynamic effectiveness (productivity) &
autonomy; robustness requires a team of MT observing R against an opposing team (2
teams of MTs to best capture the reality of a problem) => competition, but insufficient;
• 3. With two equally balanced MT teams, thermodynamic effectiveness (productivity)
& autonomy strengthen (+ MEP); robustness requires three teams (opposing MT
teams to construct R & a neutral team of freely moving independents to decide);
• 4. Given 2 teams, adding a spectrum of neutrals who invest freely (properties, ideas,
works), move freely (joining & rejecting teams), and freely observe make the greatest
contribution to robust intelligent teams, to mitigating mistakes, and to maximizing
effective & efficient autonomy (i.e., optimum MEP => RE&SR -> maximum stability).
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Possible questions & answers
• If SDT works for Energy-time, does it work for action-observation? Spatial frequencycenter of gravity? Yes, if ergodic; but, mathematically, these transformations require a
"mass" that is unknown.
• What is the optimal team size? Determined by the minimum number of team members
that enables the multitasking that solves a wdp or an idp.
• Best models of interdependence? M of interdependence -> incompleteness ≈ HUP;
implies that interdependence ≈ quantum entanglement.
• Why not focus on psychological phenomena? Methodological individualism is a static
approach to social dynamics & teams (e.g., Zell, 2014).
• What's wrong with game theory? e.g., not valid (e.g., Schweitzer et al., 2009); not reality
(Rand & Nowak, 2013, p. 416); not dynamic (Von Neumann & Morgenstern, 1953).
• Why and when are mergers problematic? A complex maneuver that attempts to reduce
reduce the costs of competition to increase a firm’s survival (Andrade & Stafford, 1999)
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Backup slides
21
Indirect Evidence from IDF: Spatial-frequency Fourier pairs:
σCOGσ1/λ ≥ 1/2
Goffeney, J., Schmidt, G.S., Dalton, J., D’Archangelo, J., &
Willis, R. (2006, Oct 29-Nov 3), Forecast Visualizations for
Terrorist Events, Poster IEEE Visualization Conference.
Based on suicide bombings in Jerusalem,
the pattern of IEDs "is always a tradeoff
between model accuracy and area
reduction that we can describe for any
given model" (Willis, R.P. (2007), The
counterinsurgency pattern assessment
(CIPA) Program 2007, NRL, p. 2.
Threat level distributions alter
significantly after spatial uncertainty is
added; Schmidt, G.S., Goffeney, J., &
Willis, R. (2007), Impact of uncertainty
on terror forecasting; NRL Review
• Example #2: σtσf >1/2: to increase power, move closer electronically to market exchange
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Selected References
• Barabási, A. L. (2012). Network science: Understanding the internal organization of complex systems. Invited talk 2012 AAAI Spring
Symposium Series, Stanford University, AAAI Publications.
• Bell, B. S., Kozlowski, S.W.J. & Blawath, S. (2012). Team Learning: A Theoretical Integration and Review. The Oxford Handbook of
Organizational Psychology. Steve W. J. Kozlowski (Ed.). New York, Oxford Library of Psychology. Vol. 1.
• Bohr, N. (1955). Science and the unity of knowledge. The unity of knowledge. L. Leary. New York, Doubleday: 44-62.
• Conant, R. C. (1976). "Laws of information which govern systems." IEEE Transaction on Systems, Man, and Cybernetics 6: 240-255.
• Cohen, L. (1995). Time-frequency analysis: theory and applications, Prentice Hall.
• Hackman (2011), HBR.
• Lawless, W. F., Llinas, James, Mittu, Ranjeev, Sofge, Don, Sibley, Ciara, Coyne, Joseph, & Russell, Stephen (2013). "Robust
Intelligence (RI) under uncertainty: Mathematical and conceptual foundations of autonomous hybrid (human-machine-robot) teams,
organizations and systems." Structure & Dynamics 6(2).
• Lawless, W.F., Akiyoshio, M., Angjellari-Dajcic, F. & Whitton, J. (2014), Public consent for the geologic disposal of highly radioactive
wastes and spent nuclear fuel, International Journal of Environmental Studies, 71(1): 41-62.
• Pfeffer, J., & Fong, C.T. (2005). "Building organization theory from first principles." Organization Science 16: 372-388.
• Presse et al., 2013, Reviews of Modern Physics.
• Rand, D.G. & Nowak, M.A. (2013), Human cooperation, Cognitive Sciences, 17(8): 413-425.
• Smith, W. K., & Tushman, M.L. (2005). "Managing strategic contradictions: A top management model for managing innovation streams."
Organizational Science 16(5): 522-536.
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