1. Emergence and complex adaptive systems

A little ant is walking on the sandy soil of Arizona looking for food. The food is meant for feeding the brood which is taken care of by other ants in the colony. Different ants have different tasks in the colony, and the ant we are following is going out of the nest every day in the search for food. The amazing aspect of ant colonies is that such a complex organization structure exists that is not controlled by the queen or a small group of bureaucratic ants. There is also no plan or to-do lists ants are following. No, the complexity of the ant colony is emerging out of the local interactions of ants. The ant we observe is following a trail of pheromones, indicating that other ants of the colony have found food nearby and dropped the pheromones on their way back to the nest. The use of pheromones is therefore a way of ants to communicate to others “follow my trail” and you may find food. The pheromone trail will evaporate at a certain speed, and will therefore only be of limited use. If other ants will not follow this trail, bring back food and add pheromones to the trail, the trail will disappear. But when the trail is enhanced by successful use of others, a highway of ants may emerge. On such a highway, we see one lane of ants empty handed following the pheromone signal, and the other lane of ants bringing food back to the nest.

There is an enormous diversity of ant-species, who have all have some differences in their social organization. Some ant-species have ant-colonies consisting of ants with different physical appearance dependent on their role in the colony: workers, foragers, soldiers, etc. In other ant-species, ants have all similar physical appearance and can switch roles. For example, when some foraging ants are killed by an ant-eater, the colony will experience a reduction of food being delivered by foraging ants, which is a signal for some ants to leave their caring for the brood role to a foraging role.

Ants change the environment which subsequently changes the behavior of other hands. This indirect influence of agents via the change of the environment is called stigmergy. Another example of stigmergy is the digital trails we develop when we interact with websites. By buying books, renting dvds or listening to music files we leave information “pheromones”. We get recommendations of other books, dvds or music that people “like you” also bought, rented or listen to. These stigmergic interactions can lead to a reinforcement of choices. Popular movies on featured on youtube tend to get even more viewers. If we want to understand how certain books, movies or songs become so popular, we need to look into the various ways choices are influenced and reinforced by others.

1.1. Emergence

Emergence is a macro-level phenomenon, like the functioning of an ant colony, as a result of local-level interactions. Like the cartoon, understanding of macro level phenomena can only be derived by studying the consequences of the behavior of micro level agents. Other examples of emergence are:

  • Path Formation. Pedestrians taking short-cuts to create new paths. Look around at the campus or in your neighborhood. Initially pedestrians walked over green grass. New pedestrians tend to use the stamped grass path instead of the pristine grass, and after many pedestrians an unpaved path is formed without any top-down design.
  • Traffic Jams. Have you ever wondered what caused the traffic jam when you reach the front of the traffic jam and you see no obvious reason for it? Sometimes it happens that traffic jams, defined a slow moving cars, move in the opposite direction of the cars. As a consequence you may enter in a traffic jam that was formed many miles again. Traffic jams can form without direct causes like accidents in the follow way. When car drivers have different desired speed levels, they need to slow down when they come close to a slower car in front of them. As a consequence other cars behind this car may need to slow down too. As we will see in later weeks, we can simulate the emergence of traffic jams with just assuming differences in desired speed of the car drivers.
  • Stadium Waves and Standing Ovations. Both are examples of emergence in stadiums and theatres. No single individual in the audience is able to create a stadium wave or a standing ovation. How do they emerge? When a few people start others may follow. How many need to start to trigger a wave and does it matter where they are sitting?
  • Flocking Behavior. A flock of birds or a school of fish seems to move in unity, like Figure 1, but no fish or bird is controlling or directing the group. What individual behaviors can lead to this flocking behavior?

These emergent phenomena can be studied by complex adaptive systems. With complex adaptive systems we refer to a group of (locally) interacting agents, who constant act and react to actions of other agents. The coherent emergent behavior that might occur in a system arises from the local interactions of the agents. In the case of a school of fish, we can explain the flocking behavior as a result of a few simple rules such as avoiding local crowding, steering towards the average heading and mean position of the school toward the average position of local fishes.

Other examples of complex adaptive systems are:

  • Stock markets. Many traders make decisions on the information known to them and their individual expectations about future movements of the market. They may start selling when they see the prices are going down (because other traders are selling). Such herding behavior can lead to high volatility on stock markets.
  • Immune systems. Immune systems consist of various mechanisms, including a large population of lymphocytes to detect and destroy pathogens and other intruders to the body. The immune systems needs to be able to recognize to detect new pathogens for the host to survive and therefore needs to be able to adapt.
  • Human and Animal brains. The neural system in the brain consist of many neurons who are exchanging information. The interactions of many neurons make it possible for me to write this sentence and ponder about the meaning of life.
  • Ecosystems. Ecosystems consist of many species which are interacts by eating other species, distributing nutrients, and pollinating plants. Ecosystems can be seen as complex food webs that are able to cope with changes in the number of certain species, and adapt – to a certain extent – to changes in climate.
  • Human societies. When you buy this new ipod that is manufactured in China, with materials derived from African soils, and with software developed by programmers from India, you need to realize that those actions are made by autonomous organizations, firms and individuals. These many individual actions are guided by rules and agreements we have developed, but there is no ruler who can control these interactions.

Complex adaptive systems and emergence are determined by what we call bottom up processes. The opposite would be a system of top-down control. In a top-down controlled system all individual components obey the rules of a central commander or a blue print. Examples are armies and bureaucratic organizations which are trained to execute orders from higher echelons. They have precise instructions what to do in order to meet the goals of the organization as a whole. Taking individual initiatives are not appreciated since it may disrupt the well trained order in the system.

1.2. An Interesting Example: Bali

An interesting example of emergence and complex adaptive systems in social systems is irrigation in Bali. Farmers who irrigate their fields in Bali have to solve a complex coordination problem. They have to solve two problems: pest outbreaks and water shortage. On one hand, control of pests is most effective when all rice fields in a watershed have the same schedule of planting rice. On the other hand, the terraces are hydrologically interdependent with long and fragile systems of tunnels, canals, and aqueducts. Therefore, to avoid water shortage the irrigators should not plant rice all at the same time.

To balance the need for coordinated fallow periods and use of water, a complex ritual system has been developed that details what rituals and actions should be done on each specific date in each organized group of farmers—called a subak. These actions are related to offerings to temples, ranging from the little temples at the rice terrace level to the temples at the regional level and all the way up to the temple of the high priest Jero Gde, the human representative of the Goddess of the Temple of the Crater Lake. Crater Lake feeds the groundwater system, which is the main source of water for irrigating in the entire watershed. These offerings were collected as a counter gift for the use of water that belonged to the gods.

There is no person who has control over the whole irrigation system. At the temple level, subak leaders come regularly together to exchange information on their irrigation experiences in their subaks. When a particular subak makes a decision that has unfavorable effects on neighboring subaks, leaders of the local temple community come together to discuss these matters. This may lead to various actions, including the treat to cut-off water supply to the disrupting subak. When seasonal rainfall is different than expected, the subak's leaders may come together to discuss alternative cropping pattern to avoid unfavorable circumstances.

The function and power of the water temples were invisible to the planners involved in promoting the Green Revolution during the 1960s. They regarded agriculture as a purely technical process. Farmers were forced to switch to the miracle rice varieties, which were predicted to lead to three harvests a year, instead of the two of the traditional varieties. Farmers were stimulated by governmental programs that subsidized the use of fertilizers and pesticides. After the governmental incentive program was started, the farmers continued performing their rituals, but they no longer coincided with the timing of rice-farming activities. Soon after the introduction of the miracle rice, a plague of plant-hoppers caused huge damage to the rice crop. A new variety was introduced, but then a new pest plague hit the farmers. Furthermore, there were problems of water shortage.

During the 1980s, an increasing number of farmers wanted to switch back to the old system, but the engineers interpreted this as religious conservatism and resistance to change. It was anthropologist Steve Lansing who unraveled the function of the water temples and was able to convince the financers of the Green Revolution project on Bali that the irrigation was best coordinated at the level of the subaks with their water temples. Lansing built an agent-based model of the interactions of subak management strategies within the ecosystem and adapting to the strategies of neighboring subaks. He showed that for different levels of coordination, from farmer level up to central control, the temple level was the level of scale where decisions could be made to maximize the production of rice. He also showed how the coordination might have been evolved as a result of local interactions. In his agent-based model, agents, representing subaks, make a decision at the end of the year which cropping pattern to use for the next year. They look at their neighboring subaks, whether one of them is doing better than themselves. If this is the case, the subak copies the strategy of the better performing subak. Within a few years, the average simulated yield increases to a level that is close to the maximum yield. The result is that there are pockets of subaks who have the same cropping pattern, solving the pest problem. These pockets of subaks are small enough to avoid serious water shortages. Steve Lansing therefore shows that a complex irrigation network is able to organize themselves by purely local interactions.

1.3. Example Models

In the Netlogo Model library under the heading Biology there is a model Ants that simulate the foraging of a colony of ants. The ant colony in the center has 3 lumps of resources in the neighborhood. In Figure 1 the purple circle is the colony, and the blue circles are food piles. The ants initially move randomly around until they find a food resource. If an ant finds a food resource, they return back to their colony leaving a trail of pheromones behind them. Ant walking randomly around may encounter pheromone trails, and they follow this in the direction away from the colony. The three food piles are at different distances from the colony. You will experience that the ants consume first the closest food pile and end with the most distant food pile. One can explore the behavior of the foraging behavior by changing the number of ants, the rate at which an ant leave pheromones behind them, and the rate the pheromones evaporate.

Figure 1: Click image to run Ant model in browser.

Another example of emergence is flocking behavior. In the biology section of the model library of Netlogo you find the model flocking. The agents check the agents in their neighborhood. If they are too close (minimum separation) to their neighbors they turn their direction (max-separate turn) to increase the separation. If an agent is not very close to its neighbors it turns its direction in line with the neighbors (max-align turn) and the agent move a bit towards its neighbors (max-cohere turn). The result is that groups of agents emerging who move around in a group. Depending on the values of the sliders a big group of agents or smaller groups of agents emerge.

Figure 2: Click image to run Flocking model in browser.

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1.4. Suggested Readings

Gordon, D. (1999), Ants at Work: How an Insect Society is Organized, New York/London: W.W. Norton & Company.

Holland, J.H. (1998) Emergence from chaos to order, Oxford University Press.

Lansing, J.S. (2006) Perfect Order, Princeton University Press.

Lansing, J.S. and J.N. Kremer (1994) Emergent Properties of Balinese Water Temples, American Anthropologist 95 (1): 97-114.

Mitchell, M. (2009) Complexity: A Guided Tour, Oxford University Press.

Waldrop, M.M. (1992) Complexity: The Emerging Science at the Edge of Order and Chaos, Simon & Schuster