In 2017 Robert Shiller, a Nobel Laureate, introduced Narrative Economics, an approach to explaining aspects of economies that are difficult to comprehend when analyzed using conventional methods: in light of narratives (i.e., stories) that participants in asset markets hear, believe, and tell each other, some observable economic factors, such as price dynamics of otherwise valueless digital assets, can be explained largely within the context of those narratives. As Shiller argues, it is best to explain and understand seemingly irrational and hard-to-explain behaviors, such as investing in highly volatile cryptocurrency markets, in narrative terms: people invest because they believe that it makes sense to do so, or have a heartfelt opinion about the prospects of the asset, and they share these beliefs and opinions with themselves and others in the form of narratives. In this paper, we address the question of how an agent-based modeling platform can be developed to be used for studying narrative economics. To do this, we integrate two very recently published developments. From the field of agent-based models of financial markets, we use the {\em PRDE} adaptive zero-intelligence trader strategy introduced by Cliff (2022), and we extend it to integrate a continuous-time real-valued nonlinear opinion dynamics model reported by Bizyaeva et al. (2022). In our integrated system, each trader holds an opinion variable whose value can be altered by interaction with other agents, modeling the influence that narratives have on an agent's opinions, and which can also be altered by observation of events in the market. Furthermore, the PRDE algorithm is modified to allow each trader's trading behavior to smoothly alter as that trader's opinion dynamically varies. Results reported for the first time here show that in our model there is a tightly coupled circular interplay between opinions and prices: changes in the distribution of opinions can affect subsequent price dynamics; and changes in price dynamics can affect the consequent distribution of opinions. Thus this paper presents a first demonstration of the reliability and effectiveness of our new agent-based modeling platform for use in studying issues in narrative economics. Python source-code for our model is being made freely available as open-source release on GitHub, to allow other researchers to replicate and extend our work
