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Complexity Series: Abnormality as the Norm (pt.2)

Jun 4, 2021 | Blog

In the previous entry of this series I broke down complexity in the abstract, detailing how the uncertainty principles inherent in business-to-business complexity don’t necessarily equate to increased risk if approached with the correct tools. However, to understand where we can develop it is necessary to mark where the road so far has led and indicate how current risk management fails. To that end we’ll dive a little deeper into the specifics of a complex business approach, observing how tightness and looseness relate to efficiency and adaptability in current business structures. From there, how modern analytics techniques can improve risk control and thereby business effectiveness. 

Tangled in Knots

It’s necessary to clarify what is meant by “tightness and looseness” before approaching risk assessment. Pioneered by Mauro F. Guillén, these terms describe the degree to which groups in complex economies are coupled. Coupling refers to a dyadic relational dependency, in which at least one organization of the pair requires an asset provided by the other in order to function. An assembly company is coupled with component manufacturing; Google Adsense to websites that feed them user data. Tightness / looseness then describes the nature of that dependency from a systemic perspective, in which a tightly coupled relation is one in which a failure on one axis of the dyad triggers failure of the other, and a loosely coupled relationship does not have that property. 

It’s here that it becomes necessary to deviate from Guillén’s definitions, as his conception was designed to describe the overall systemic nature of the dynamic. For our purposes however it’s necessary to narrow the scope to individual businesses. Tight / loose coupling will then describe only one business’ perspective on the relationship, rather than the net effect. From the previous example then Google would describe the partnership with individual websites as loosely coupled, because while they gain revenue and user feedback from the relationship there are enough consumers of their service that failure of one website does not meaningfully impact Google’s Adsense’s success. The business running the website however might consider this a tight coupling, as there are few service providers in that space that can even attempt to approach the efficiency and reliability of Adsense. 

The Light at the End is Another Train

Coupling itself is nothing new to business: it is the centralizing principle upon which economies exist. The sticking point here is that things rarely go as planned — in complex economies, specifically, predicting how likely (and when) situations may change becomes impossible. Tight coupling in this scenario is then a highly unattractive arrangement, as the necessarily increased exposure to exogenous risk associated with tightness paired with high volatility in the environment creates quite the toxic compound. The reactions to this phenomenon are typically one of two approaches: two-pronged tightness or radical looseness. Unfortunately each of these “remedies” to the above problem of risk control brings with it their own set of issues.

In two-pronged tightness, parties follow a pattern similar to an I lose, you lose negotiation strategy. Parties attempt to control risk by doubling-down on it and building agreements that ensure the failure of one party to the contract transfers to equivalent failure to the other, i.e. causing both parties to consider the deal a tight coupling. Whether through rich trade-in-tasks, exclusivity agreements, or severe indemnification clauses, parties try to stabilize their footing by making the counterparty engage in the same degree of risk (and thus have equal incentive to protect against default). While in principle this might ease the psychological concern of the counterparty “not taking this as seriously” it does nothing in practice to mitigate exposure to uncontrollable factors. Ensuring the other ship also capsizes does not protect one against the storm.

The second strategy seems more promising, by approaching all agreements with “looseness” in mind. In practice this would mean maximizing the number of alternatives should an agreement fail (BATNA), as well as minimizing their own liability to enable party mobility. This is the guiding principle behind the labor structure of the “gig economy” popularized by Uber, DoorDash and others; minimize risk exposure by maximizing labor distribution. The cracks in this approach have widened considerably since their inception, as increasing looseness in coupling can have disastrous effects regarding production quality assurance and setting hard caps on business efficiency. Distributed sourcing to guarantee loose coupling vis-a-vi the gig economy provides an inverted mirror of Fordism, which increased specialization to exchange tight coupling for productivity. 

The Oak and the Reed

Clearly neither of the two reactionary approaches is a long-term viable solution to the problem of economic complexity, as either extreme generates repercussions easily as problematic as the initial starting point of uncertainty. Linear strategic approaches such as these are inevitably going to fail, because the environment is of a fundamentally nonlinear nature. The only practical solution from a business-to-business perspective is to engage each exchange situation as a novel event. Utilizing stochastic agent-based AI modeling to measure the curvature of uncertainty surrounding a particular negotiation arrangement allows for maximum business adaptation to individualized risk features unique to the exchange scenario and counterparty dynamics, ensuring the perfect balance of tightness (efficiency) and looseness (risk control). 

About Intellext™

Intellext is an AI startup that is revolutionizing the way contracts are negotiated, accelerating time to close, and improving deal terms. Intellext’s Intelligent Negotiation Platform™ eliminates the complexities of contract redlines and stakeholder collaboration and optimizes deal terms by applying machine learning during the negotiation process.

Joseph Hunt
Written by Joseph Hunt

Behavioral Researcher with formal training in psychology, philosophy, and user experience. Passionate about modeling behavioral and decision-making processes in pragmatic, actionable ways. BA in Psychology, San Jose State University

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