# Reputation blackboard systems

## TL;DR

This paper investigates blackboard systems for solving cryptarithmetic puzzles, demonstrating that reputation-based blackboards outperform standard ones and revealing insights into their efficiency and limitations in cooperative problem solving.

## Contribution

It introduces a minimal model of reputation blackboard systems and compares their performance to standard blackboards and independent search methods.

## Key findings

- Reputation blackboards outperform standard blackboards and independent search.
- The computational cost distribution is exponential for all heuristics.
- Optimal performance for reputation blackboards occurs with a single agent, indicating limited support for cooperation.

## Abstract

Blackboard systems are motivated by the popular view of task forces as brainstorming groups in which specialists write promising ideas to solve a problem in a central blackboard. Here we study a minimal model of blackboard system designed to solve cryptarithmetic puzzles, where hints are posted anonymously on a public display (standard blackboard) or are posted together with information about the reputations of the agents that posted them (reputation blackboard). We find that the reputation blackboard always outperforms the standard blackboard, which, in turn, always outperforms the independent search. The asymptotic distribution of the computational cost of the search, which is proportional to the total number of agent updates required to find the solution of the puzzle, is an exponential distribution for those three search heuristics. Only for the reputation blackboard we find a nontrivial dependence of the mean computational cost on the system size and, in that case, the optimal performance is achieved by a single agent working alone, indicating that, though the blackboard organization can produce impressive performance gains when compared with the independent search, it is not very supportive of cooperative work.