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Developing Online Games. An Insiders Guide Authors: Mulligan J., Petrovsky B. Published year: 2003 Pages: 132-133/230 |
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DetectionBecause the difference between desirable and undesirable behavior is often subtle, detecting when such behavior occurs is often a challenge. Typically, it's easy enough to see the final consequence of undesirable behavior ( angry parents, lost customers); it's often much more difficult to establish the root cause. More significantly, there will always come a point where someone needs to make a value judgment as to whether a particular act was acceptable or not within a particular context. This judgment may occur far in advance of the actual act (as, for example, prohibitions that are embedded in program code), or it may take place immediately after the event (as when someone reports a violation). It may in some cases even be made long after the fact, when someone notices a statistical pattern or analyzes a set of user complaints. Fortunately, many of the more serious abuses tend to be consistent behavior patterns on the part of individuals. These behaviors tend to be a reflection of the underlying value system of the player, and that is not something that changes quickly. Bruce Schneier, security consultant and author of Applied Cryptography , once said this about online games : "It's not important to detect every cheat. It's important to detect every cheater." Thus, it is in many cases possible to get a history of significant actions and judgments associated with a particular individual, and use that information in making future judgments . The two primary means of data collection will be:
Unfortunately, neither of these sources of information is reliable, but there is hope because they are unreliable in different ways and can be corroborated in order to gain a more accurate picture. In particular, witnesses can be biased or untrustworthy but are really good at interpreting what they see in terms of values. Automated agents are all too easily misled (mistaking legitimate behavior for impermissible behavior, for example) but are incapable of dissembling or shading the truth. 
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VerificationSimply detecting the presence of an undesirable behavior is often not sufficient to take corrective action, especially if the corrective action has a large potential negative impact or cost. A trust value needs to be assigned to the detection report and supporting or refuting evidence gathered. For simple cases, where the cost of corrective action is low, the reports can be collected by an automated system and the corrective action taken automatically. An example is profanity filtering; there is no need to report to a human customer representative each time someone uses an impermissible word or phrase. For more complex cases, especially where the players in question have a considerable stake, a trusted human may be required to intervene. It is likely that there will be several "tiers" of trust, where there will be a large outer layer of "slightly trusted" individuals and an inner core of "highly trusted" individuals. For extremely sensitive cases, there may be a "commit/no-commit" protocol that requires multiple trusted representatives to be involved, so that no single individual (either inside or outside the company) can gain great advantage by manipulating the system. When combining witness testimony with data gathered by automated agents , care must be taken to ensure that the proper context of the event is maintained . A sentence taken in isolation can easily be misinterpreted. The customer service (CS) user interface should be designed so that arbiters can easily access and comprehend the relevant details of an incident without wasting too much time searching through log files. |
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Developing Online Games. An Insiders Guide Authors: Mulligan J., Petrovsky B. Published year: 2003 Pages: 132-133/230 |
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