hi, i would prefer earlier - 8 or so sicne i do not have itnernet at home yet
On Thu, Sep 12, 2013 at 1:22 PM, Suresh Marru <[email protected]> wrote: > 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. > > > > > >
