We often think of intelligent action in terms of a number of ideas: goal-directedness, belief acquisition, planning, prioritization of needs and wants, oversight and management of bodily behavior, and weighting of risks and benefits of alternative courses of action. These assumptions presuppose the existence of the rational subject who actively orchestrates goals, beliefs, and priorities into an intelligent plan of action. (Here is a series of posts on “rational life plans”; link, link, link.)
It is interesting to discover that some simple adaptive systems apparently embody an ability to modify behavior so as to achieve a specific goal without possessing a number of these cognitive and computational functions. These systems seem to embody some kind of cross-temporal intelligence. An example that is worth considering is the spatial and logistical capabilities of the slime mold. A slime mold is a multi-cellular “organism” consisting of large numbers of independent cells without a central control function or nervous system. It is perhaps more accurate to refer to the population as a colony rather than an organism. Nonetheless the slime mold has a remarkable ability to seek out and “optimize” access to food sources in the environment through the creation of a dynamic network of tubules established through space.
The slime mold lacks beliefs, it lacks a central cognitive function or executive function, it lacks “memory” — and yet the organism (colony?) achieves a surprising level of efficiency in exploring and exploiting the food environment that surrounds it. Researchers have used slime molds to simulate the structure of logistical networks (rail and road networks, telephone and data networks), and the results are striking. A slime mold colony appear to be “intelligent” in performing the task of efficiently discovering and exploiting food sources in the environment in which it finds itself.
One of the earliest explorations of this parallel between biological networks and human-designed networks was Tero et al, “Rules for Biologically Inspired Adaptive Network Design” in Science in 2010 (link). Here is the abstract of their article:
Abstract Transport networks are ubiquitous in both social and biological systems. Robust network performance involves a complex trade-off involving cost, transport efficiency, and fault tolerance. Biological networks have been honed by many cycles of evolutionary selection pressure and are likely to yield reasonable solutions to such combinatorial optimization problems. Furthermore, they develop without centralized control and may represent a readily scalable solution for growing networks in general. We show that the slime mold Physarum polycephalum forms networks with comparable efficiency, fault tolerance, and cost to those of real-world infrastructure networks—in this case, the Tokyo rail system. The core mechanisms needed for adaptive network formation can be captured in a biologically inspired mathematical model that may be useful to guide network construction in other domains.
Their conclusion is this:
Overall, we conclude that the Physarum networks showed characteristics similar to those of the [Japanese] rail network in terms of cost, transport efficiency, and fault tolerance. However, the Physarum networks self-organized without centralized control or explicit global information by a process of selective reinforcement of preferred routes and simultaneous removal of redundant connections. (441)
They attempt to uncover the mechanism through which this selective reinforcement of routes takes place, using a simulation “based on feedback loops between the thickness of each tube and internal protoplasmic flow in which high rates of streaming stimulate an increase in tube diameter, whereas tubes tend to decline at low flow rates” (441). The simulation is successful in approximately reproducing the observable dynamics of evolution of the slime mold networks. Here is their summary of the simulation:
Our biologically inspired mathematical model can capture the basic dynamics of network adaptability through iteration of local rules and produces solutions with properties comparable or better than those real-world infrastructure networks. Furthermore, the model has a number of tunable parameters that allow adjustment of the benefit-cost ratio to increase specific features, such as fault tolerance or transport efficiency, while keeping costs low. Such a model may provide a useful starting point to improve routing protocols and topology control for self-organized networks such as remote sensor arrays, mobile ad hoc networks, or wireless mesh networks. (442)
Here is a summary description of what we might describe as the “spatial problem-solving abilities” of the slime mold based on this research by Katherine Harman in a Scientific American blog post (link):
Like the humans behind a constructed network, the organism is interested in saving costs while maximizing utility. In fact, the researchers wrote that this slimy single-celled amoeboid can “find the shortest path through a maze or connect different arrays of food sources in an efficient manner with low total length yet short average minimum distances between pairs of food sources, with a high degree of fault tolerance to accidental disconnection”—and all without the benefit of “centralized control or explicit global information.” In other words, it can build highly efficient connective networks without the help of a planning board.
This research has several noteworthy features. First, it seems to provide a satisfactory account of the mechanism through which slime mold “network design intelligence” is achieved. Second, the explanation depends only on locally embodied responses at the local level, without needing to appeal to any sort of central coordination or calculation. The process is entirely myopic and locally embodied, and the “global intelligence” of the colony is entirely generated by the locally embodied action states of the individual mold cells. And finally, the simulation appears to offer resources for solving real problems of network design, without the trouble of sending out a swarm of slime mold colonies to work out the most efficient array of connectors.
We might summarize this level of slime-mold intelligence as being captured by:
- trial-and-error extension of lines of exploration
- localized feedback on results of a given line leading to increase/decrease of the volume of that line
This system is decentralized and myopic with no ability to plan over time and no “over-the-horizon” vision of potential gains from new lines of exploration. In these respects slime-mold intelligence has a lot in common with the evolution of species in a given ecological environment. It is an example of “climbing Mt. Improbable” involving random variation and selection based on a single parameter (volume of flow rather than reproductive fitness). If this is a valid analogy, then we might be led to expect that the slime mold is capable of finding local optima in network design but not global optima. (Or the slime colony may avoid this trap by being able to fully explore the space of network configurations over time.) What the myopia of this process precludes is the possibility of strategic action and planning — absorbing sacrifices at an early part of the process in order to achieve greater gains later in the process. Slime molds would not be very good at chess, Go, or war.
I’ve been tempted to offer the example of slime mold intelligence as a description of several important social processes apparently involving collective intentionality: corporate behavior and discovery of pharmaceuticals (link) and the aggregate behavior of large government agencies (link).
On pharmaceutical companies:
So here’s the question for consideration here: what if we attempted to model the system of population, disease, and the pharmaceutical industry by representing pharma and its multiple research and discovery units as the slime organism and the disease space as a set of disease populations with different profitability characteristics? Would we see a major concentration of pharma slime around a few high-frequency, high profit disease-drug pairs? Would we see substantial under-investment of pharma slime on low frequency low profit “orphan” disease populations? And would we see hyper-concentrations around diseases whose incidence is responsive to marketing and diagnostic standards? (link)
On the “intelligence” of firms and agencies:
But it is perfectly plain that the behavior of functional units within agencies are only loosely controlled by the will of the executive. This does not mean that executives have no control over the activities and priorities of subordinate units. But it does reflect a simple and unavoidable fact about large organizations. An organization is more like a slime mold than it is like a control algorithm in a factory. (link)
In each instance the analogy works best when we emphasize the relative weakness of central strategic control (executives) and the solution-seeking activities of local units. But of course there is a substantial degree of executive involvement in both private and public organizations — not fully effective, not algorithmic, but present nonetheless. So the analogy is imperfect. It might be more accurate to say that the behavior of large complex organizations incorporates both imperfect central executive control and the activities of local units with myopic search capabilities coupled with feedback mechanisms. The resulting behavior of such a system will not look at all like the idealized business-school model of “fully implemented rational business plans”, but it will also not look like a purely localized resource-maximizing network of activities.
Here is a very interesting set of course notes in which Prof. Donglei Du from the University of New Brunswick sets the terms for a computational and heuristic solution to a similar set of logistics problems. Du asks his students to consider the optimal locations of warehouses to supply retailers in multiple locations; link. Here is how Du formulates the problem:
* Assuming that plants and retailer locations are fixed, we concentrate on the following strategic decisions in terms of warehouses.
- Pick the optimal number, location, and size of warehouses
- Determine optimal sourcing strategy
- Which plant/vendor should produce which product
- Determine best distribution channels
- Which warehouses should service which retailers
The objective is to design or reconfigure the logistics network so as to minimize annual system-wide costs, including
- Production/ purchasing costs
- Inventory carrying costs, and facility costs (handling and fixed costs)
- Transportation costs
As Du demonstrates, the mathematics involved in an exact solution are challenging, and become rapidly more difficult as the number of nodes increases.
Even though this example looks rather similar to the rail system example above, it is difficult to see how it might be modeled using a slime mold colony. The challenge seems to be that the optimization problem here is the question of placement of nodes (warehouses) rather than placement of routes (tubules).