Narrative as/and Complex System/s

Marie-Laure Ryan

What does it mean to say "narrative is complex" or "narrative is a complex system"? Complex compared to what: ordering a beer, following a recipe, proving a theorem? Are all narratives complex or only some of them — the ones considered "literary" or, to extend the idea to all media, the ones considered artistic? Is the scientific notion of complexity useful to narrative theory, or should narrative theory develop its own notion of complexity? Such are the questions that I propose to address in this chapter.

In systems theory complexity is associated with concepts such as emergence, nonlinearity, decentralized control, feedback loops, recursion, self-organization, simulation, and distributed intelligence and with formulas such as "the whole is more than the sum of its parts" or "small events can have vast consequences" (the famous butterfly effect). The so-called science of complexity is a loosely defined field that extends into many disciplines and covers a wide variety of phenomena: the organization of ant colonies and beehives; the mode of operation of the brain; the fluctuations of the stock market; the evolution of species; the development of the weather; the organization of the World Wide Web; and the functioning of the immune system, to name only a few. What these systems have in common, according to scientist Melanie Mitchell, is (a) collective behavior: they are made of a number of individual elements that follow relatively simple rules, without the guidance of a leader; (b) signal and information processing: all these systems produce signals, exchanged both internally (i.e., between their components) and externally (between the system and its external environment); and (c) adaptation: these systems change their behavior according to the circumstances, to improve their chance of survival (2011, 12–13). Though there is no unanimously accepted definition of complexity, nor a fixed set of transdisciplinary criteria for measuring and comparing complexity across systems, these conditions lead Mitchell to the following definition: a complex system is "a system in which large networks of components with no central control give rise to complex collective behavior, sophisticated information processing and adaptation via learning or evolution" (13). (Note, however, the redundancy, which points to the difficulty of defining complexity: a complex system is one that "give[s] rise to complex collective behavior" and "sophisticated [or complex] information processing.")

Other scientists dispute the necessity of adaptation: the late John Holland, a leading authority in the field, argued that there are two types of complexity: complex physical systems, or CPS, and complex adaptive systems, or CAS (2014, 6–9). An example of CPS is a cellular automaton, such as John Conway's famous Game of Life: in a cellular automaton, a set of rules is applied over and over again to dots arranged on a grid, causing the global state of the system to change and resulting in unpredictable visual patterns, but the rules remain the same, and the system evolves on its own without input from the designer ("Conway's Game of Life" 2018). In a CAS, by contrast, "the elements, usually called agents, learn to adapt in response to interaction with other agents" (Holland 2014, 8). Moreover, "as the agents adapt to each other, new agents with new strategies usually emerge" (9).

Whether or not the individual components of a system are capable of adaptation, the trademark of complex systems is the lack of a central controlling entity, comparable to the CPU of a computer, that pursues deliberate goals. Out of the simple rules followed by the components of complex systems arise (or more precisely, emerge) patterns and behaviors that could not be predicted by simply looking at the rules or by solving an equation. Therein resides the difference between a simple, linear system and a complex system. With a simple system there is a formula (an equation) that makes it possible to calculate the state of the system at a given time without calculating all the previous states. For instance, if a car moves at a steady speed of a hundred kilometers per hour, we know how far it has gone at a certain time by giving a value to t in the equation x =100t, where t represents the number of hours the car has been traveling and x the distance, without having to calculate how far it has gone at time t-1, t-2, and so on. But with a complex system, such as a cellular automaton, there is no equation that predicts future states. To know what the global state of the system will be like at time t, it is necessary to compute its state at all preceding times, by recursively applying its rules to their own output (or new rules if there is adaptation). In other words, the state of a complex system at a future time t cannot be directly calculated, but it can be obtained through a simulation. There is consequently a close relation between emergence and simulation: we run a simulation to discover emergent properties; conversely, a property is emergent, when it can be discovered only through a simulation.

An often cited example of a complex, emergent system is the ant colony (Johnson 2001). An ant colony consists of large numbers of individuals who follow very...