Hi all, I am okay with either 8pm or 9pm US eastern time.
Thanks, Nadeem On Thu, Sep 12, 2013 at 11:08 PM, Ra myaa <[email protected]> wrote: > 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. >> > >> > >> >> >
