# Reputation Algorithms

## Content Contributor Reputation Model

#### Metrics:

**Total Number of Articles Published (N):**The cumulative count of articles a contributor has published in each Olas Information Market.**Sum of all Outcome Scores given to Articles Written (AS):**The total of all outcome scores received for all the articles written by the contributor across all Information Markets.

#### Reputation Algorithm:

The **Reputation Score (RS)** for a Content Contributor is calculated using the following formula:

$\boxed{ RS \, = \, \,\dfrac{ AS }{ N } }$

**Fact-Checker Reputation Model**

**Fact-Checker Reputation Model**

**Metrics**:

**Metrics**:

**Total Number of Valid Issues Raised (V):**Cumulative count of valid issues identified by a fact-checker.**Number of Valid Issues Submitted in Each Category of Severity (S):**Number of valid issues submitted, categorized by severity.**Low Severity (LS):**Minor Issues that have less impact or are easily rectifiable.**Multiplier Factor (M1):**1.2

**Medium Severity (MS):**Issues that have a moderate impact or may require a moderate effort to rectify.**Multiplier Factor (M2):**1.5

**High Severity (HS):**Significant Issues that have a high impact or may require substantial effort to rectify.**Multiplier Factor (M3):**2

**Number of Unique & Valid Issues Submitted (UV):**Number of issues submitted that are both unique & valid.**Unique & Valid (UV):**Uniqueness is defined as an issue that was identified and submitted by only one Fact-Checker.**Multiplier Factor (M4):**2

**Reputation Algorithm**:

**Reputation Algorithm**:

The Reputation Score (RS) for a Fact-Checker is derived using a weighted sum formula. Each metric is multiplied by a corresponding weight, and the sum of these products forms the RS. The formula is articulated as follows:

$\boxed{ Preliminary \, Score \, = \, V \, + \, ( \,LS \, \times \, M1 \,) \, + \, ( \, MS \, \times \, M2 \, ) \, + \, ( \, HS \, \times \, M3 \, ) \,+ \, ( \, UV \, \times \, M4 \, ) }$

$\boxed{ Reputation \, Score \, = \, \,\dfrac{ Preliminary \, Score }{ Total \, Number \, Of \, Issues \, Submitted } }$

**where:**

$M1, M2, M3,$ and $M4$ are the weighting factors assigned to each metric based on its relative importance.

**Judge Reputation Model**

**Judge Reputation Model**

**Metrics**:

**Metrics**:

**Total Number of Judging Panels Completed (JP):**The cumulative count of judging panels a judge has completed.**Sum of all Voting Accuracy Scores Across All Votes Submitted in Judging Panels (VA):**The total of accuracy scores received for all votes submitted by the judge across various judging panels.

**Reputation Algorithm**:

**Reputation Algorithm**:

The **Reputation Score (RS)** for a Judge is computed using the following formula:

$\boxed{ RS \, = \, \,\dfrac{ VA }{ JP } }$

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