Since man built the first structure to remove himself from the whims of the elements, people have developed more and more sophisticated forms of protection. Modern investors, financial analysts, economists, regulators and governments have put their faith in models in the hope that their mathematical constructs will be able to duplicate reality sufficiently to protect themselves from disasters. Despite the size of the hardware, the complexity of the mathematics, or the efforts of the marketing departments, the problem is that the models never really work. Storms blow down houses, earthquakes compromise nuclear reactors and markets panic. But might nature herself provide something better?

 The main problem with market panics following unexpected and unpredicted events is the lack of information. The earthquake in Japan was unprecedented. Nuclear experts presently do not know enough to make accurate predictions about the situation. The problems in the Middle East are too many and varied. The recent regime changes in ongoing turmoil were off the radar screens of area specialists.

 Should we blame the failure of the cognoscenti on the dreaded black swans? Like the collapse of 2008, is the Japanese earthquake or the so-called Arab spring the phenomenon so rare that it should not be considered within analytical probabilities? There is no question that these events were rare, but if we have learned anything over the past few years, it should be that these black swans tend to swim by every day.

 Rather than try to dismiss the events as unprecedented, it would perhaps be a better idea to consider how we analyze these events. For example in economics many of the risk models were based on the economic concept of efficient markets. Sadly these markets do not exist. They are based on two false assumptions. These are that complete, correct information is available and that markets will act rationally to create efficient pricing.

 Other models are also flawed partially because of their origin. The famous Black–Scholes formula is based on a concept from physics, Brownian motion, which describes the random movement of particles in the air, easily observed by watching dust in a ray of sunshine in late afternoon. But physics does not translate very well to human endeavor. Black-Scholes pricing models underestimates extreme moves, assumes no transaction costs and continuous trading. None of which exist in any market.

 Any model based on concepts from physics and mathematics also requires exact measurements. No market participant in possession of exceptionally valuable information would ever have any intention of providing such information with the precision the models demand. None of this stops banks, brokers, investors and regulators from relying on these models every day.

 Markets function much like the human eye. The retina has insufficient sensors, rod and cone cells, to provide the brain with accurate information. So the brain fills in the blanks with memories. In a similar manner markets make up for the lack of information. What is important for markets is not in fact information, which is unavailable, but perceived reality.

 To find a model for perceived reality, we look to another branch of science in this case biology. Certain animals like bees, ants, fish and birds travel in swarms, schools and flocks. Although a single animal’s intelligence is miniscule, the swarm itself can often solve complex computational problems. For example the problem of the traveling salesman who must find the shortest route to visit a number of cities, is daily solved y ants who easily find the shortest route between the food source, and their nests.

 Even more intriguing is the behavior of bees. When searching for food or a good location to locate a new colony, scout bees reconnoiter large areas and then return to the hive. When they land they do a specific type of waggle dance. The purpose of the dance is to communicate the quality of a given location to other scout bees. If the dance is particularly long, more scout bees will be recruited to check out the site. Like markets, If enough bees form the same opinion, the swarm follows.

 Fish in a school and birds in a flock receive information which is neither direct nor pervasive. Most are acting only on moving in the direction of the other animals while not getting too close to others. The school is moving according to a “leader” who actually is aware of either food or a threat.

 The behavior of fish, bees, and birds applies to all manner of organisms, from individual cells in a tissue to voters in a democratic election. To develop models with better predictive value, it might be better to observe the perceptions of other sentient beings than be mislead by random bits of dust.