Fig. 5. The curves represent quality in the stabilised regime for L1 and L2.

![0_image_0.png](0_image_0.png)

Until cheaters remain below the threshold of 60%, reputation allows for quality to reach higher values than happens in the complementary condition. The truth circulates faster providing social evaluations which are coherent with the reality.

Coherence between information and reality, shared by a larger amount of agents in L2, increases the trust and the market is more efficient. Over the threshold of 60false information floods in, hence, social evaluations, circulating faster with reputation, are often distorted with respect to reality. computational models which are strictly related with social evaluations (e.g.

opinions [7,8] and their dynamics [24,23]).

## 8 Acknowledgements

A particular thanks to Geronimo Stilton and the Hypnotoad.

## References

1. Frederic Amblard, Arnaud Casteigts, Paola Flocchini, Walter Quattrociocchi, and Nicola Santoro. Time-varying graphs and social network analysis: Temporal indicators and metrics. *Artificial Intelligence and Simulation of Behaviour (AISB 11)*
York (England), 2011.

2. Arnaud Casteigts, Paola Flocchini, Walter Quattrociocchi, and Nicola Santoro.

Time-varying graphs and dynamic networks. 10th International Conference on Adhoc Networks and Wireless (ADHOC-NOW), 2011.

3. J.S. Coleman. *Foundations of Social Theory*. Harvard University Press, London, 1990.

4. Rosaria Conte and Mario Paolucci. *Reputation in Artificial Societies: Social Beliefs* for Social Order. Springer, October 2002.

5. R. Dunbar. The social brain hypothesis. *Evolutionary Anthropology*, 6:178–190, 1998.

6. Karen K. Fullam and K. Suzanne Barber. Learning trust strategies in reputation exchange networks. In AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, pages 1241–1248, New York, NY, USA, 2006. ACM.