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74 Artificial Intelligence for the Internet of Everything

wanted to understand how intelligent adaptive behaviors are related to the
formal dynamics and structures among agents. We studied how organiza-
tional structure influences the processes of searching for sets of good deci-
sions and stabilizing around good decisions once they are discovered
(Rivkin & Siggelkow, 2003). The search and stability issues are conceptually
identical to the exploration-exploitation tradeoffs afforded by free-energy
minimization, allowing us to examine the alignments between energy-based
computational mechanisms and discrete human decision-making processes
analyzed by Rivkin and Siggelkow.
Second, we posit that the free-energy principle explains the empirically
observed behaviors of business organizations. Unlike network-based theo-
ries of cities (e.g., Schl€apfer et al., 2014), which are developed and tested
using extensive quantitative data about social and economic transactions,
development of behavior models for business organizations has lagged
behind since the data on operations of business enterprises (e.g., communi-
cation channels, personnel assignments, and task outcomes) is often propri-
etary and not available. However, it has been empirically observed that as
companies mature and grow, they attempt to maximize profits (utility) at
the expense of innovation (entropy, disruption or disorder), placing increas-
ing emphasis on rules, regulations, and other forms of bureaucratic control
over its members, impeding market adjustments and leading to their even-
tual demise (West, 2017). The free-energy principle explains how placing
more emphasis on utility versus entropy makes the system brittle and unable
to adapt to a changing environment.
Finally, we wanted to identify what implications the free-energy mini-
mization principle had on the design of agents that constitute the effective
members of a high-performance team.
4.3.2 Problem Definition

The formal definition of a distributed decision-making problem (Rivkin &
Siggelkow, 2003) is as follows. Assume that a team of M agents seeks an N-
dimensional binary decision vector d¼[d 1 ,…,d N ], where d i 2{0,1}, to
maximize its additive objective function, i.e.,
∗ X P
d ¼ arg max C d ðÞ ¼ c j d j ,
j¼1
N
d2 0, 1g
f
where d j is a subset of decision variables, and P is the number of these
subsets. Component reward functions c j (d j ) encode dependencies between
decisions in subset d j (such as local and team-level rewards). The general