Game of collusions

Abstract

A new model of collusions in an organization is proposed. Each actor $a_{i=1,\cdots,N}$ disposes one unique good $g_{j=1,\cdots,N}$. Each actor $a_i$ has also a list of other goods which he/she needs, in order from desired most to those desired less. Finally, each actor $a_i$ has also a list of other agents, initially ordered at random. The order in the last list means the order of the access of the actors to the good $g_j$. A pair after a pair of agents tries to make a transaction. This transaction is possible if each of two actors can be shifted upwards in the list of actors possessed by the partner. Our numerical results indicate, that the average time of evolution scales with the number $N$ of actors approximately as $N^{2.9}$. For each actor, we calculate the Kendall's rank correlation between the order of desired goods and actor's place at the lists of the good's possessors. We also calculate individual utility funcions $\eta_i$, where goods are weighted according to how strongly they are desired by an actor $a_i$, and how easily they can be accessed by $a_i$. Although the individual utility functions can increase or decrease in the time course, its value averaged over actors and independent simulations does increase in time. This means that the system of collusions is profitable for the members of the organization

Wealth Rheology

“…Many ideas behind agent-based modelling have been derived from concepts like kinetic theory [ 17 , 18 ], scattering [ 19 ], rate equations [ 20 ], random matrix theory [ 21 , 22 ], Brownian motion [ 23 ] which were developed in statistical physics. Using this type of ideas, one was able to model wealth or income distributions [ 24 ], dynamics of wealth inequality [ 25 , 26 ], wealth concentration [ 27 ], structure emergence [ 28 , 29 ], economic instability and corruption mechanisms [ 30 , 31 , 32 ], systemic risk in economic networks [ 33 ], emergence of heavy tails in wealth and income distributions [ 24 , 34 ], and herding behaviour [ 35 ], or to analyse statistical behaviour or rational agents [ 36 ].…”

Wealth rheology

“…Many ideas behind agent-based modelling have been derived from concepts like kinetic theory [17,18], scattering [19], rate equations [20], random matrix theory [21,22], Brownian motion [23] which were developed in statistical physics. Using this type of ideas one was able to model wealth or income distributions [24], dynamics of wealth inequality [25,26], wealth concentration [27], structure emergence [28,29], economic instabilities and corruption mechanisms [30][31][32], systemic risk in economic networks [33], emergence of heavy-tails in wealth and income distributions [24,34], and herding behaviour [35], or to analyse statistical behaviour or rational agents [36].…”