Texas treasury notes and market manipulation, 1837-1842

This paper examines the course of the value of the paper money issued by the Republic of Texas in New Orleans, from 1837 to 1842, using a newly-constructed weekly time series of quotations, and focusing on the possibility of market manipulation. Specifically, during 1841, misleading information concerning a possible foreign loan reached New Orleans three different times. The first time, the information substantially raised the value of Texas Treasury Notes. The second time, the information raised the value, but to a lesser extent. The third time, the information had no impact.     

Fake News and hospitality research

The proliferation of the “Fake News” transcend political agendas and has infiltrated the hospitality industry. This Editorial Viewpoint is intended to drive research that challenges “fake news” and takes into consideration the uniqueness of the hospitality industry. This could provide a foundation for theoretical interest in this phenomenon in attempt to prefigure the contour of “fake news”. The main goal is to initiate a discourse on matters pertaining to “fake news” that will help us rethink and inquire what constitutes knowledge in order to better understand the past, present, and future of hospitality research.     

Misinformation,disinformation, and fake news: Cyber risks to business

Misleading information is an emerging cyber risk. It includes misinformation, disinformation, and fake news. Digital transformation and COVID-19 have exacerbated it. While there has been much discussion about the effects of misinformation, disinformation, and fake news on the political process, the consequences of misleading information on businesses have been far less, and it can be argued insufficiently, examined. The article offers a primer on misleading information and cyber risks aimed at business executives and leaders across an array of industries, organizations, and nations. Misleading information can have a profound effect on business. I analyze different misleading information types and identify associated cyber risks to help businesses think about these emerging threats. I examine in general the cyber risk posed by misleading information on business, and I explore in more detail the impact on healthcare, media, financial markets, and elections and geopolitical risks. Finally, I offer a set of practical recommendations for organizations to respond to these new challenges and to manage risks.     

Electoral competition with fake news

Misinformation pervades political competition. We introduce opportunities for political candidates and their media supporters to spread fake news about the policy environment and perhaps about parties’ positions into a familiar model of electoral competition. In the baseline model with full information, the parties’ positions converge to those that maximize aggregate welfare. When parties can broadcast fake news to audiences that disproportionately include their partisans, policy divergence and suboptimal outcomes can result. We study a sequence of models that impose progressively tighter constraints on false reporting and characterize situations that lead to divergence and a polarized electorate.     

Social media networks,fake news,and polarization

We study how the structure of social media networks and the presence of fake news affects the degree of misinformation and polarization in a society. For that, we analyze a dynamic model of opinion exchange in which individuals have imperfect information about the true state of the world and exhibit bounded rationality. Key to the analysis is the presence of internet bots: agents in the network that spread fake news (e.g., a constant flow of biased information). We characterize how agents’ opinions evolve over time and evaluate the determinants of long-run misinformation and polarization in the network. To that end, we construct a synthetic network calibrated to Twitter and simulate the information exchange process over a long horizon to quantify the bots’ ability to spread fake news. A key insight is that significant misinformation and polarization arise in networks in which only 15% of agents believe fake news to be true, indicating that network externality effects are quantitatively important. Higher bot centrality typically increases polarization and lowers misinformation. When one bot is more influential than the other (asymmetric centrality), polarization is reduced but misinformation grows, as opinions become closer the more influential bot’s preferred point. Finally, we show that threshold rules tend to reduce polarization and misinformation. This is because, as long as agents also have access to unbiased sources of information, threshold rules actually limit the influence of bots.