The Decision Making Process of Property Crime Offenders Research Proposal

The Decision Making Process of Property Crime Offenders - Research Proposal Example All social behaviors occur through a process of decision making, so does criminality. As seen earlier on the decision-making process is cognitive, and as odd as it may seem even criminal offenders go through the process of decision making. It is  a continuous process based on an individual’s interaction with the environment that ends when an acceptable or suitable solution is reached. The main motivation of criminology is the avoidance of pain and pursuit of pleasure. Property crimes have a greater percentage of all crimes that happen. If there is an intention to commit a crime, this is burglary. If none such breaking and entering into a place alone called illegal trespass of property. (Nee.C, 2003) Academic and policy-driven interest in situational crime prevention has stimulated a variety of offenses-specific research from the 1980s onwards. This has resulted in considerable development in both ‘grounded’ methodological approaches and our understanding of the burglar’s behavior and decision-making during the criminal event (Nee, 2003). It is of great importance to master the psychology of the person who commits the crime. Find out why, how and for what reason. It is also important to find out the various phases that the criminal offender goes through during different stages of the offense.

Reject inference applied on large data sets Research Paper

Reject inference applied on large data sets - Research Paper Example However, this assumption does not hold true in the case of application scoring. The modeling data set becomes inherently biased if the customers that are perceived to be â€Å"bad† are approved while those that are perceived to be â€Å"good† are rejected. It is a matter of fact that the only population’s performance that is known is for the approved, which apparently does not perform the same way as the rejected population, hence the rejection of this population is rather questionable. Notably, the selection bias does not take place if further bad rates are estimated using the approved population in the model alone. Nonetheless, considering that the model is applicable to the whole population in order to decide who to reject and who to decline, the bias becomes a very important consideration. Correction and accounting for this sample bias is achieved by use of rejecting inference techniques. In view of this, a gap is present in any statistical model when known Good-Bad (KGB) of the approved population of loan applicants is used, because of the high sampling bias error that occurs. As a matter of fact, any analysis of characteristics is biased as a result of the ‘cherry selection’ of prospective good customers. If bad rates across the whole population is truly described by the characteristics, then it is evident that the rate of approval by the same characteristics should be inversely related. For a case in point, if the customer has serviced loans without any problem for the last one year, then the subdivision’s general bad rate should be moderately small, and the approval rate from this subdivision should be large. Nevertheless, customers that hold at least 4 bad loans in the previous one year should be treated as a high credit risk. As such, any approval in this segment should be assigned a variety of other ‘good’ characteristics to su persede offensive