Social Behavior Bias and Knowledge Management Optimization

Individuals can manage and process novel information only to some degree. For instance, it was recently shown that when performing a perceptual novel task, there is a cost to high information flow. Hence, there is a balance between too little information (i.e. not getting enough to finish the task), and too much information (i.e. a processing constraint). In other words, actions are selected so as to have a high yet stable amount of novel information on the environment. Combining these new findings to a formal mathematical description of efficiency of novel information processing results in an inverted U-shape, wherein too little information is not effective to solving a problem, yet too much information is also detrimental as it requires more processing power than available. The optimal place to be is at a specific point, such that novel information is managed at a high signal-to-noise ratio. However, in an information flooded economic environment, it has been shown that humans are rather poor at managing information overload, which results in far from optimal performance. In this work we speculate that this is due to the fact that they are actually trying to maximize the wrong thing, e.g. maximizing monetary gains, while completely disregarding information management principles that underlie their decision-making. In other words, people do not internalize the fact that too much information is sometimes detrimental, since it confounds the decision-making process. Thus, in a social decisionmaking environment, when information flows from one individual to another, people may “misuse” the abundance of information they receive. Using the model of individual novelty management, and the empirical statistical nature of investors’ inclination to information, we have derived the social network information flow dynamics and have shown that the “spread” of people’s position along the inverted U-shape of efficient information management leads to an unstable and inefficient macro-scale dynamics of the network’s performance. This was in turn validated through a global inverted U-shape, observed in the macro-scale network performance. We suggest that changing the distribution of people’s position along the information management axis can have drastic effects on the network performance. Two basic manipulations can be considered from a physical system analogy: (i) changing the “temperature” of the system, i.e. either raising it to create a more diverse spread or lowering it to make a more homogenous network; (ii) by lowering the system’s temperature one can then tune the distribution center to be more in the optimal efficient information management regime.