The videos I recommend anyone watch to understand the importance of this are listed below:

Opinion dynamics modeling in society (part 1)

How do governments determine policy priorities?

The Hidden Trump Model - Opinion Dynamics w/ Social Desirability Bias - H. Zontine & S. Davies

Tauchain is unique because it can aggregate opinions into consensus and toward synthesis

For those who do not understand what Tauchain is trying to do we have to understand that in the beta network of Tauchain consensus = synthesis. Synthesis in this case is program synthesis. In other words the product of consensus is the software. The consensus emerges based on discussion. During this discussion the opinions will be broadcast in such a way that agreements will be reached. These agreements will form the basis of the specification from which program synthesis can produce or output the software.

The problem Tauchain will face is the same problem which any preference aggregation optimization network will face. In other words just because people have preferences and try to express those preferences it does not mean that these preferences will be effectively expressed. In my other post I identified a specific problem which is summed up in the question on whether or not you can effectively aggregate preferences if there is false preferences being expressed? This problem has been called preference falsification but in general it seems to make the case for why privacy is necessary.

Tauchain promises to scale discussion which is great but the problem is some discussions cannot be had at all. Some discussions are so controversial that people cannot even attempt to start them. For these discussions only privacy would allow for the discussion to take place. Of course this doesn't mean discussions will be equally productive even if privacy was allowed.

What is so important about modeling opinion dynamics?

Opinions have to be formed. How are opinions formed? If a agent must make a decision to be pro or con some specific issue then can we model this process? The utility of this is explored in the video below:

The mathematics of influence is the title of the video above. In other words it might be possible to use Tau not just to scale discussion but to discuss how to better discuss. To improve opinion formation or to at least understand how opinions are being formed in the network could be of utility. The more participants in the discussion, the bigger the network, the more important the mathematical models could become.

How do we deal with problems such as bias? This could include racism, sexism, etc? Any kind of cognitive bias can influence opinion formation but how? Ultimately if we do not understand how to model or think about these things mathematically then it's going to be much harder to examine in depth what is going on. For people who are math inclined and who understand the danger of bias in AI then this may be of interest.

The voter model is specifically interesting. It examines how opinions on who to vote for forms. Under this model a node is picked at random from the network (a neighbor) and the opinion of that neighbor is adopted by the node. Which opinion wins out? The high degree nodes (hubs) which have the highest probability of being connected to. This could mean a lot for an election or for opinion shaping. To me this would resemble the thought leader paradigm where the most connected thought leader expresses their opinion in the group and because a lot of people are connected to them in some direct or indirect way their opinion holds a lot more weight. If those thought leaders are zealots (will not change their mind no matter what new evidence they receive) then these individuals have even more influence on the outcome and on opinion formation.

References

1. Sobkowicz, P. (2017). Opinion dynamics model based on cognitive biases. arXiv preprint arXiv:1703.01501.

2. Fernández-Gracia, J., Suchecki, K., Ramasco, J. J., San Miguel, M., & Eguíluz, V. M. (2014). Is the voter model a model for voters?. Physical review letters, 112(15), 158701.

3. Popkin, G. (2014). Focus: Voter model works for us elections. Physics, 7, 40.