3.2. Practitioner’s Tips: Reputation Is Tricky

When you begin designing a reputation model and system using our graphical grammar, it may be tempting to take elements of the grammar and just plug them together in the simplest possible combinations to create an Amazon-like rating and review system, or a Digg-like voting model, or even a points-based karma incentive model as on StackOverflow. In practice—"in the wild," where people with myriad personal incentives interact with them both as sources of reputation and as consumers—the implementation of reputation systems is fraught with peril. In this section, we describe several pitfalls to avoid in designing reputation models.

3.2.1. The Power and Costs of Normalization

We make much of normalization in this book. Indeed, in almost all of the reputation models we describe, calculations are performed on numbers from 0.0 to 1.0, even when normalization and denormalization might seem to be extraneous steps. Here are the reasons that normalization of claim values is an important, powerful tool for reputation: