Page 1

Metaphors of human collaboration A vision about the creative workplace of the future by Christoph Magerl We humans distinguish ourselves clearly from the rest of all species by certain features – at least this is what we think. Among these features we possess a rich culture, a complex and intangible something, which makes us pass on knowledge, behaviors and conceptions over generations. A part of this culture is the way we work together – collaboration. Day by day we arrive in our offices, workshops and construction sites. Yet it is hardly understood neither by the individual, nor the maker of the workplace, what we actually do there together. Yet, why and how we collaborate with others and the way we do this ,is a key element of our human achievements. How can the maker of workplaces understand this essential part of human culture? How can we anticipate the future? For this we do not have to re-invent the wheel. Many behaviors in the human and animal world give interesting insights into the collaboration between individuals.

Metaphors of human collaboration

Flocks, herds and schools Man has always admired the beautiful behaviors of animal swarms. Bird, fish, gazelle – In a seemingly magic way those individuals perform choreographies in hundreds, thousands or even hundreds of thousands. They seem to hold a formula about how to collaborate in large populations. They all seem to share a common goal.

Swarm behaviour A swarm can be looked at in two ways. When focusing on the outside, the swarm is seen as an entity. Its behavior is treated as one big organism. For mathematicians this is the Eulerian approach, other professions would say systemic approach. When focusing on the inside, the swarm is a collection of individuals. Here the rules, which the individual follow are important. To mathematicians this is the Lagrangian approach; for others rather the inidividual-centered approach. Once the system is seen from the outside, the other time from the inside.

The Lagrangian swarm For the last sixty years researchers have been trying to understand the mystery and beauty of animal swarms. Some focused on fish schools others on birds. The results were to some extent the same. Individuals act on a certain set of rules. All individuals in the swarm have those rules. As a consequence they function together to create a swarm. Craig Reynolds, known for simulating swarms on the computer, established the following rules: A bird moves in the same direction as its neighbor and remains close to it while avoiding collision. Other scientists added further rules. Reynolds and his colleagues tried to define a behavior in three-dimensional space. This does not directly help to determine human behavior in offices. Movement in space is

Metaphors of human collaboration

the least problem for the collaboration of knowledge workers. Thus the movement of animals in a three-dimensional space has to be translated to a multi-dimensional virtual space for human concerns. Our ideas and thoughts can be seen as flying birds; when they collide we agree. This abstract interpretation of an animal swarm indeed offers insights about human collaboration.

The Gaia hypothesis In 1972, James Lovelock, a British scientist and environmentalist, published an astonishing theory: The Gaia hypothesis, wherein he described the earth as one big organism. With a number of theoretic examples he has shown that the earth with all living and non-living matter stays in an equilibrium, that is maintained by the matter itself. He derived that every organism contains other organisms. The earth organism contains organisms – humans. Humans contain organisms – microorganisms. Microorganisms likewise contain other organisms – mitochondria. All of these organisms work together to survive. This Gaia theory is useful as it tells something about the complexity of collaborating individuals in swarms. A swarm can be the swarm of a swarm of a swarm. It also tells us that there is practically no border to collaboration. In an office context it does not stop at the door of the building, or at the border of the city nor anywhere else.

The limits of beauty The swarms of fish schools or those of birds are often taken for inspiration purposes simply because they are visually impressive. This does not mean that there is always the actual right message available. The life of a human being is more complex than that of a fish, especially when he is performing a mentally demanding task with others.

Metaphors of human collaboration

Social insects Common examples on social insects – scientifically called hymenoptera – are bees, wasps, ants and termites. These insects have been researched thoroughly the last 60 years. In 1950 Norbert Wiener considered it to be fascist to compare human beings with ants. He argued, that they are simple and stupid, whereas humans are complex and intelligent. In his eyes we cannot expect much from them. 19 years later Herbert Simon, an important figure in the science of the 20th century, stated almost the opposite: Not the ant itself is the key to the astonishing achievements of this species but the ant’s behavior in its environment.

Random creativity Simple insects like ants are indeed simple by themselves. An ant left alone shows random behavior. It runs around without plan and will eventually die. But as soon as it senses certain signals from the environment or other ants it reacts on them in a distinguished way. In an ant state every ant acts upon these schemes. Is there too much information in the environment the ant society becomes stuck in narrow patterns. Without information there is chaos. Hence the ant community best works on “the edge of chaos”, as scientists call it: The moment where everything is possible while still being productive.

Stigmergy In the same year of Herbert Simon’s publication of the ant’s power in interaction with its environment, Pierre-Paul Grassé, researching about termites, came up with a word for it: Stigmergy. This is an indirect communication between individuals – or any agent to be correct. An ant alters the environment so that the next ant passing by can react on it. There are two types of stigmergy. In Cue-based stigmergy an ant discovers a cue in the environment, like a pile of sand. In

Metaphors of human collaboration

sign-based stigmergy the ant discovers a signal from another ant. For ants and termites it works with a complex set of pheromones. Bees do a wiggle dance to pass on information. These cues let the individual carry on work another individual has begun before. This enables termites to build two-meter high towers, bees to build hives and ants to build their fantastic networks.

Emergence Simple social animals are powerful in their multitude, but most important because of their emergent behavior. This behavior emerges through messages from the environment. Other individuals or natural circumstances create these messages. This emergence means that there is no real plan involved; no plans what to do or build, no leader and no hierarchy. The shape of a termite pyramid is determined by the architecture of the termites’ communication with their environment. Altering the communication would change the look entirely. Most likely there would not be any building because it is rather difficult to achieve this productive edge of chaos. Those communication patterns evolved in hundred thousands of years.

Collaboration in a human scale Already Herbert Simon’s correction of Wiener’s theory shows that we humans resemble more to ants than we thought. By seeing the human society with human eyes it seems as if we were more complex, intelligent and self-determined, but considering the scale of our earth-embracing society the resemblance may be surprisingly close. We humans are a connected society of 7 billion people. In this scale we may behave similarly to ants. This makes it interesting to look at ants when designing for human collaboration.

Metaphors of human collaboration

Neural networks A biologists view Small, specialized cells, called neurons, process information in viurtually every individuum more sophisticated than microbia. Several branches reach out from their cell body to other neurons, called dendrites. As an extension of the cell body one axon leads out of the cell body and sends information to its end, the synapses. These transmit signals to other dendrites. The core sends out a signal if the sum - precisely the integral - of input signals reaches a certain threshold. Our brain uses this simple function in a network of millions of cells: input process - output. This physically enables human intelligence.

A mathematician’s view A neural network is usually defined from a mathematician’s perspective. He sees nodes and connections that form a network. Both, nodes and connections can have a vast set of characteristics. A certain way of processing information, hence the behavior of the network, is the result.

Neural collaboration What happens in a neural network is not collaboration but communication and information processing. Then again, the non-hierarchical and unpredictable way neurons organize their network is astonishing and instructive. A neural network shows patterns of emergence like an ant society. The single entity is simple but many entities together are incredibly flexible, resilient and perform very complex tasks.

Metaphors of human collaboration

Human collaboration Hierarchy Hierarchies in working environments are already decreasing today. The examples above offer the vision that hierarchy may even go extinct. Workers in an ant state do not obey any hierarchy, they are specialized: A queen does queen work; a harvester does harvester work and so on. A neural network does not follow a hierarchy either nor does a bird flock or a school of fish. Hence the future may come without hierarchy. Clearly, this does not mean that five ideas clash upon each other and the solution will be to toss a coin. Each idea has a different grade of persuasiveness and each person a different grade of receptiveness. Thus the solution may emerge.

Think big Intelligent social animals like wolf packs have a hierarchy; elephants too. But once the population becomes larger it takes effort to maintain the hierarchy. Thus in fish schools, herds, flocks, swarms and especially states of social insects behavior rather emerges chaotically. These swarms act like a superorganism, where the interior functioning is rather unknown. Only one hundred years ago we humans were scattered over the planet in hundreds of small societies. Now we are about to grow into one big super-organism too. We are connected worldwide in a powerful communication network. Now we are closer to an ant state than ever.

Metaphors of human collaboration

Emergent collaboration To perform work today, a plan is made and sub-tasks are delegated. This is simple in small organization. In large ones; as for the construction of a Boeing 747 it means an awful amount of work. It is of course radical to say that we do not plan anything anymore and just see where we might end up. But to a certain extent this may be exactly what makes us productive in the future. Especially in the field of creative knowledgeable people this freedom may result in better solutions. Why just looking at the performance of one person or one office? Looking at what the entire organization as superorganism yields might be more interesting.

Let go! This would result in losing control and accessibility. In 1927 Werner Heisenberg published the uncertainty principle, which basically says: The closer you look the more you mess it up. In our current world we are obsessed with knowing and controlling everything. This behavior leaves as much freedom for good ideas as it gives freedom to a football player who is wired up with measuring cables. It is a brave step but letting go ideas might emerge into what we have always waited for. It might reveal the nature of human collaboration, as we have never seen it before.

References Swarm Intelligence, James Kennedy et al., Morgan Kaufmann publishers, 2001. The human use of human beings, Norbert Wiener, The Riverside Press, 1950. The sciences of the artificial, Herbert Simon, MIT press, 1996. Flocks, herds and schools: A distributed behavioural model, Craig Reynolds, in Computer Graphics 21, 1987. Geometry for the selfish herd, Wiliam D. Hamilton, Dissertation at Imperial College, 1970. Swarms, phase transitions and collective intelligence, Mark M. Millonas, in Artificial Life III, Addison Wesley, 1994. Gaia: A new look at life on earth, James Lovelock, Oxford University Press, 1979. Termitologia I, II, III, Pierre-Paul GrassĂŠ, Masson, 1982-86. Life at the edge of chaos, Christopher Langton, in Artificial life II, Addison Wesley, 1991. GĂśdel, Escher, Bach: an eternal golden braid, Douglas Hofstater, Basic Books, 1979. Elephants dont play chess, Rodney brooks, in Robotics and Autonomous Systems 6, 1990.

Christoph Magerl

Metaphors of human collaboration  

A short essay about metaphors from biology to inspire solutions for human collaboration.