Discovering the Dynamics of Smart Business Networks

In an earlier paper ,was discussed the necessary evolution from smart business networks, as based on process need satisfaction and governance, into business genetics [1] based on strategic bonds or decay and opportunistic complementarities. This paper will describe an approach and diffusion algorithms whereby to discover the dynamics of emergent smart business network structures and their performance in view of collaboration patterns over time. Some real life early analyses of dynamics are discussed based on cases and date from the high tech sector. Lessons learnt from such cases are also given on overall smart network dynamics with respect to local interaction strategies, as modelled like in business genetics by individual partner profiles, goals and constraints. It shows the weakness of static "business operating systems", as well as the possibly destabilizing clustering effects amongst nodes linked to filtering, evaluation and own preferences.dynamics;network performance;smart business networks;SBN;business genetics

Discovering the dynamics of smart business networks

Earlier research discussed the necessary evolution from smart business networks, as based on process need satisfaction and governance, into business genetics [1] based on strategic bonds or decay and opportunistic complementarities. This paper will describe an approach and diffusion algorithms whereby to discover the dynamics of emergent smart business network structures and their performance in view of collaboration patterns over time. Some real life early analyses of dynamics are discussed based on cases and date from the high tech sector. Lessons learnt from such cases are also given on overall smart network dynamics with respect to local interaction strategies, as modelled like in business genetics by individual partner profiles, goals and constraints. It shows the weakness of static “business operating systems”, as well as the possibly destabilizing clustering effects amongst nodes linked to filtering, evaluation and own preferences.smart business networks; business genetics; network performance; SBN; dynamics

Evolutionary prisoner's dilemma game on a square lattice

A simplified prisoner's game is studied on a square lattice when the players interacting with their neighbors can follow only two strategies: to cooperate (C) or to defect (D) unconditionally. The players updated in a random sequence have a chance to adopt one of the neighboring strategies with a probability depending on the payoff difference. Using Monte Carlo simulations and dynamical cluster techniques we study the density $c$ of cooperators in the stationary state. This system exhibits a continuous transition between the two absorbing state when varying the value of temptation to defect. In the limits $c \to 0$ and 1 we have observed critical transitions belonging to the universality class of directed percolation.Comment: 6 pages including 6 figure

Correlation of Positive and Negative Reciprocity Fails to Confer an Evolutionary Advantage: Phase Transitions to Elementary Strategies

Economic experiments reveal that humans value cooperation and fairness. Punishing unfair behavior is therefore common, and according to the theory of strong reciprocity, it is also directly related to rewarding cooperative behavior. However, empirical data fail to confirm that positive and negative reciprocity are correlated. Inspired by this disagreement, we determine whether the combined application of reward and punishment is evolutionarily advantageous. We study a spatial public goods game, where in addition to the three elementary strategies of defection, rewarding, and punishment, a fourth strategy that combines the latter two competes for space. We find rich dynamical behavior that gives rise to intricate phase diagrams where continuous and discontinuous phase transitions occur in succession. Indirect territorial competition, spontaneous emergence of cyclic dominance, as well as divergent fluctuations of oscillations that terminate in an absorbing phase are observed. Yet, despite the high complexity of solutions, the combined strategy can survive only in very narrow and unrealistic parameter regions. Elementary strategies, either in pure or mixed phases, are much more common and likely to prevail. Our results highlight the importance of patterns and structure in human cooperation, which should be considered in future experiments

Economic Incentives and Social Preferences: A Preference-based Lucas Critique of Public Policy

Policies and explicit private incentives designed for self-regarding individuals sometimes are less effective or even counterproductive when they diminish altruism, ethical norms and other social preferences. Evidence from 51 experimental studies indicates that this crowding out effect is pervasive, and that crowding in also occurs. A model in which self-regarding and social preferences may be either substitutes or complements is developed and evidence for the mechanisms underlying this non-additivity feature of preferences is provided. The result is a preference-based analogue to the Lucas Critique restricting feasible implementation to allocations that are supportable given the effect of incentives on preferences.public goods, behavioural experiments, social preferences, second best, motivational crowding, explicit incentives

Back to the Future: A Century of Compensation

What were the hot compensation issues and practices over the past century? Does history offer any lessons that may inform our compensation decisions in the future? To answer these questions, we reviewed newspapers and business publications from the past 100 years. To highlight changes in compensation systems during that time, we selected four topics to examine in detail in this paper: compensation\u27s role in the changing nature of the deal; the evolution of pay-for-performance; the emergence of benefits; and the bellwethers of compensation systems. Four lessons for the future are drawn. These include: End the search for the one right compensation strategy; Understand what in the context matters; Continue pragmatic experimentation, and Support continuous learning about compensation. Readers are invited to delve into the history of compensation to discover what they take away for the future