Hi All, How about a google hangout today at 9pm US eastern time (6 pm pacific), 6.30 am IST?
Suresh On Sep 10, 2013, at 1:10 PM, Arvind Verma <[email protected]> wrote: > Hi Everyone > This looks very promising and I think we should discuss this together. > Suresh- can we meet and go through what has been done with Ramyaa, Adam and > Nadeem through Skype? We should also discuss how we can turn this into a > publishable paper. > Except M W afternoons-evenings I am available at all other times > Arvind > > > On Tue, Sep 10, 2013 at 10:29 AM, Nadeem Anjum <[email protected]> > wrote: > Hi All, > > I have enabled the agent to commit crimes, which is decided by a number of > factors like reward associated with the crime, risk involved, number of times > the spot has been visited, whether police is present in vicinity, whether the > guardian of the spot is away . The simulation is being run for two weeks. For > the first week the agent commits no crime and just gets acquainted with the > spots. In the second week the agent commits crime based on the above factors. > > You can have a look at it here: http://gw76.iu.xsede.org/criminfo/ > > A sample simulation has been saved by the name - "crimetest" . You can click > on load and enter - crimetest - to view it. You can also run a new simulation > on cities in taiwan (say taipei). > > We have four global variables: > • risk_avoidance : a value between 0 and 1 (generated randomly) > • profit_seeking : a value between 0 and 1 (generated randomly) > • maxprofitpossible : set to 100 for this simulation > • maxriskpresent : set to 100 for this simulation > Crime Spots are labelled as "CS-n [reward, risk, p ,g ,f]" on the map, where > > where, > • n is the id of the crime spot, > • reward is profit value associated with the spot - this is initialized > as a random number generated between 0 and maxprofitpossible > • risk = inherent_risk + > risk_due_to_the_number_of_times_the_spot_is_visited > • inherent_risk is initialized as a random number generated between 0 > and maxriskpresent. > • risk_due_to_the_number_of_times_the_spot_is_visited = > num_times_visited * 0.02 * maxriskpresent > • p = probability that police is present in the vicinity of the spot > • g = probability that the guardian of the spot is not present at the > spot > • f = number of times crime has been committed at this spot > The algorithm for deciding whether the agent commits a crime is as follows: > > //check patrolling police presence > tmp = random num between 0 and 1 > if(tmp > p){ //patrolling police not present > tmp = random num between 0 and 1 > r = risk > if(tmp < g){ // guardian is away > r = r/2 //risk is reduced to half . this is only for this > particular case. the risk value //stored for > this spot remains unchanged > } > tmp = random num between 0 and 1 > if( tmp > risk_avoidance*r/maxriskpresent.){ //agent decides to > take the risk > tmp = random num between 0 and 1 > if(tmp < profit_seeking*reward/maxprofitpossible){ //reward > is good enough > commit_crime > } > } > } > > Thanks, > Nadeem. > >
