Computational Population Dynamics

  

computation -
Function: noun
1 a : the act or action of computing : CALCULATION b : the use or operation of a computer
2 : a system of reckoning
3 : an amount computed
computational - adjective

population -
Etymology: Late Latin population, from Latin populus
1 a : the whole number of people or inhabitants in a country or region b : the total of individuals occupying an area or making up a whole c : the total of particles at a particular energy level -- used especially of atoms in a laser
3 a : a body of persons or individuals having a quality or characteristic in common b (1) : the organisms inhabiting a particular locality (2) : a group of interbreeding organisms that represents the level of organization at which speciation begins
4 : a group of individual persons, objects, or items from which samples are taken for statistical measurement
Merriam-Webster Online Dictionary


Persistent Social Groups

Humans, zebras, geese, ants, bacteria, all form social groups. Taken as a snapshot any population of social animals will have several groups in it. However, a snapshot taken at a different time may show totally different groups. Social associations change from minute to minute, hour to hour, day to day. Some groups tend to change more, while others, less. Some just loose or gain a few members at a time while others break up into many smaller groups and reassemble again. Other still just reappear every now and then. Which social groups are important, significant, stable? What does it mean for a group to persist over time? And over what time? We try to answer these questions algorithmically, modeling populations as dynamic graphs. We give a formal general definition of persistence of a social group and try to analyze what it means in various contexts and what algorithms are appropriate for various applications. Our main application right now is the social structure of zebras at the Mpala Conservancy in Kenya, Africa, and onagers in India. This project is a collaboration between Dan Rubenstein's group from Princeton, Jared Saia from University of New Mexico, and me.

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