A network model of mass media opinion dynamics

The coexistence of diverse opinions is necessary for a pluralistic society in which people can confront ideas and make informed choices. The media functions as a primary source of information, and diversity across news sources in the media forms the basis for wider discourse in the public. However, due to numerous economic and social pressures, news sources frequently co-orient their content through what is known as intermedia agenda-setting. Past research on the subject has examined relationships between individual news sources. However, to understand emergent behaviour such as opinion diversity, we cannot simply analyse individual relationships in isolation, but instead need to view the media as a complex system of many interacting entities. The aim of this thesis is to develop and empirically test a method for understanding the network effects that intermedia agenda-setting has on the diversity of expressed opinions within the media. Utilising latent signals extracted from news articles, we put forward a methodology for inferring networks that capture how agendas propagate between news sources via the opinions they express on various topics. By applying this approach to a large dataset of news articles published by globally and locally prominent news organisations, we identify how the structure of intermedia networks is indicative of the level of opinion diversity across various topics. We then develop a theoretical model of opinion dynamics in noisy domains that is motivated by the empirical observations of intermedia agenda formation. From this, we derive a general analytical expression for opinion diversity that holds for any network and depends on the network's topology through its spectral properties alone. Finally, we validate the analytical expression in a linear model against empirical data. This thesis aids our understanding of how to model emergent behaviour of the media and promote diversity

Are All Patent Examiners Equal? The Impact of Examiner Characteristics

Building on insights gained from interviewing administrators and patent examiners at the United States Patent and Trademark Office (USPTO), we collect and analyze a novel dataset on patent examiners and patent outcomes. This dataset is based on 182 patents for which the Court of Appeals for the Federal Circuit (CAFC) ruled on validity between 1997 and 2000. For each patent, we identify a USPTO primary examiner, and collect historical statistics derived from their entire patent examination history. These data are used to explore a number of hypotheses about the connection between the patent examination process and the strength of ensuing patent rights. Our main findings are as follows. (i) Patent examiners and the patent examination process are not homogeneous. There is substantial variation in observable characteristics of patent examiners, such as their tenure at the USPTO, the number of patents they have examined and the degree to which the patents that they examine are later cited by other patents. (ii) There is no evidence that examiner experience or workload at the time a patent is issued affects the probability that the CAFC finds a patent invalid. (iii) Examiners whose patents tend to be more frequently cited tend to have a higher probability of a CAFC invalidity ruling. The results suggest that all patent examiners are not equal, and that one of the roles of the CAFC is to review the exercise of discretion in the patent examination process.

Analysis of Music Genre Clustering Algorithms

Classification and clustering of music genres has become an increasingly prevalent focusin recent years, prompting a push for research into relevant algorithms. The most successful algorithms have typically applied the Naive Bayes or k-Nearest Neighbors algorithms, or used Neural Networks to perform classification. This thesis seeks to investigate the use of unsupervised clustering algorithms such as K-Means or Hierarchical clustering, and establish their usefulness in comparison to or conjunction with established methods

The impact of noise and topology on opinion dynamics in social networks

We investigate the impact of noise and topology on opinion diversity in social networks. We do so by extending well-established models of opinion dynamics to a stochastic setting where agents are subject both to assimilative forces by their local social interactions, as well as to idiosyncratic factors preventing their population from reaching consensus. We model the latter to account for both scenarios where noise is entirely exogenous to peer influence and cases where it is instead endogenous, arising from the agents' desire to maintain some uniqueness in their opinions. We derive a general analytical expression for opinion diversity, which holds for any network and depends on the network's topology through its spectral properties alone. Using this expression, we find that opinion diversity decreases as communities and clusters are broken down. We test our predictions against data describing empirical influence networks between major news outlets and find that incorporating our measure in linear models for the sentiment expressed by such sources on a variety of topics yields a notable improvement in terms of explanatory power

Endogenous Appropriability

Innovation’s private value is typically less than its social value, so to encourage innova- tion, researchers in economics and strategy have focused on how innovators can appropri- ate value across different economic, institu- tional, and strategic environments ( Teece 1986; Gans and Stern 2003 ) . For sta rt-ups without pre-existing assets such as manufacturing capa- bilities or brand reputation, researchers have identified appropriability through formal intel- lectual property protection ( which we will refer to as a “control” approach ) and first-mover com- petitive advantage ( which we will refer to as an “execution” approach ) as distinct paths. Most research has taken a start-up’s appro- priability regime as exogenous, i.e., environ- mentally determined ( e.g., control-orientation in biotechnology, and execution-orientation in Internet software ) . This paper develops a simple model highlighting the interplay between con- trol and execution as alternative routes to appro- priability. Whereas a control strategy allows an innovator to forestall imitation once established, control itself takes time, and so can delay market entry. In contrast, an execution strategy is pre- mised on taking advantage of the benefits aris- ing from rapid market entry such as customer learning, reputational advantages, or coordina- tion on a standard. Does the start-up shield itself from competition through investing in entry barriers or does it invest in dynamic capabilities allowing it to “get ahead, stay ahead”? We derive two main results. First, the choices of control and execution are strategic substi- tutes. Notably, when the ability to learn from early customer feedback in the marketplace is sufficiently high, an entrepreneur might choose not to invest in intellectual property protection even if such protection is costless and effective. Second, the choice between control and execu- tion interacts with other key strategic choices such as whether to pursue a narrow or broad customer segment, or whether to commercialize a “minimal viable product” versus a more robust version. Innovation appropriability depends not only on the instruments available to an innova- tor, but on how those instruments interact with each other as part of the firm’s ( endogenous ) entrepreneurial strategy (see Ching, Gans, and Stern 2016; Gans, Stern, and Wu 2016 )

A network perspective on intermedia agenda-setting

In Communication Theory, intermedia agenda-setting refers to the influence that different news sources may have on each other, and how this subsequently affects the breadth of information that is presented to the public. Several studies have attempted to quantify the impact of intermedia agenda-setting in specific countries or contexts, but a large-scale, data-driven investigation is still lacking. Here, we operationalise intermedia agenda-setting by putting forward a methodology to infer networks of influence between different news sources on a given topic, and apply it on a large dataset of news articles published by globally and locally prominent news organisations in 2016. We find influence to be significantly topic-dependent, with the same news sources acting as agenda-setters (i.e., central nodes) with respect to certain topics and as followers (i.e., peripheral nodes) with respect to others. At the same time, we find that the influence networks associated to most topics exhibit small world properties, which we find to play a significant role towards the overall diversity of sentiment expressed about the topic by the news sources in the network. In particular, we find clustering and density of influence networks to act as competing forces in this respect, with the former increasing and the latter reducing diversity

Nonlinear Circuits

Contains research objectives and reports on two research projects