Speaker: Dr Ridhi Kashyap (Associate Professor of Social Demography, Nuffield College, University of Oxford)

Date: Thursday 5 March 2020, 3:00pm - 4:30pm

Room: Department of Methodology, Columbia House, LSE (COL.8.13)

Speaker: Dr Maurice Chiodo (Fellow and College Teaching Officer at University of Cambridge)

Date: Friday 28 February 2020, 11:00am - 12:30pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)

Abstract: For the last 20 years it has become increasingly obvious, and increasingly pressing, that mathematicians should be taught some ethical awareness so as to realise the impact of their work. This extends even to those more highly trained, like graduate students and postdocs. But which mathematicians should we be teaching this to, what should we be teaching them, and how should we do it? In this talk I’ll explore the idea that all mathematicians will, at some stage, be faced with ethical challenges stemming from their work, and yet few are ever told beforehand.

Speaker: Dr Moritz Osnabrügge (Postdoctoral Research Officer, Department of Government, LSE)

Date: Thursday 20 February 2020, 3:00pm-4:30pm

Room: Department of Methodology (COL.8.13)

Abstract: When and why do politicians use emotive rhetoric in parliamentary speeches? Research has shown that emotions matter in political campaigns, but we know less about their effect on legislative behaviour. In this paper, we argue that politicians use emotive rhetoric to primarily appeal to voters.We thus expect that politicians are more likely to use emotive rhetoric in parliamentary speeches, the larger the general audience. Our analysis covers one million parliamentary speeches held in the British parliament, the House of Commons, in the period from 2001 until 2019. We use a dictionary-based method to measure emotive rhetoric, combining the Affective Norms for English Words dictionary with word embeddings techniques to create a domain-specific dictionary. Our analysis reveals that the level of emotive rhetoric has increased since 2015 with the start of the Brexit debate. Importantly, we also show that emotive rhetoric is more pronounced in high-profile legislative debates, such as Prime Minister’s Questions Time. These findings suggest that politicians use emotive rhetoric strategically to appeal to a wider audience.

Speaker: Dr Jordi Blanes i Vidal (Associate Professor, Department of Management, LSE)

Date: Thursday 23 January 2020, 3:15pm - 4.30pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)

Abstract: Communication is integral to organisations, and yet field evidence on the relation between communication and worker productivity remains scarce. We argue that a core role of communication is to transmit information that helps co-workers do their job better. We build a simple model in which the optimal amount of communication trades-off this benefit against the time cost incurred by the sender, and use it to derive a set of empirical predictions. We then exploit a natural experiment in an organisation where problems arrive and must be sequentially dealt with by two workers. For exogenous reasons, the first worker can sometimes communicate face-to-face with their colleague. Consistently with the predictions of our model we find that: (a) the second worker works faster (at the cost of the first worker having less time to deal with incoming problems) when face-to-face communication is possible, (b) this effect is stronger when the second worker is inherently slower, busier, and dealing with more urgent tasks, and (c) the effect is also stronger for teams that are more homogenous and located closer to each other. Our findings illustrate how workers in teams adjust the amount of mutual communication to its costs and benefits.

Speaker: Rachel Free (Partner, CMS Cameron McKenna Nabarro Olswang LLP)

Date: Thursday 28 November 2019, 1.00pm - 3.00pm

Room: NAB.1.04, New Academic Building, LSE

Abstract: Patent protection for AI inventions is on the increase with over 34000 patent families related to artificial intelligence having been published.  However, AI algorithm patents are typically very detailed and it is often difficult to tell whether competitors are using the patented AI algorithm.  In the lecture there will be discussion of this problem and how it can be addressed.  In the case of inventions created by AI algorithms there is a potential problem since in the eyes of the law inventors are human.  In the lecture there will be discussion of this issue and how it can be addressed.  

Speaker: Dr Eugenie Dugoua (Assistant Professor in Environmental Economics, LSE)

Date: Thursday 14 November 2019, 3.15pm - 4.45pm

Room: Department of Methodology, Columbia House, LSE (COL.8.13)

Abstract: Can international environmental agreements induce innovation on green technologies? It is possible that international negotiations succeed only once technological solutions are available. In this case, agreements would help diffuse such technologies rather than fostering their development. I provide the first quantitative evidence that the Montreal Protocol, and its following amendments to protect the ozone layer, triggered a large increase in research and innovation on alternatives to ozone-depleting molecules. To do this, I use the full-text of patents and scientific articles to construct new panel data of the yearly number of published documents about these molecules. I implement a difference-in-differences strategy (DiD) and a synthetic control method (SCM) using hazardous air pollutants as control units. To compare molecules’ chemical and industrial characteristics, I use topic modelling, a machine-learning-based quantitative text analysis technique, to construct descriptive variables. The SCM estimates that the post-Montreal regime caused a 144% increase in patents and a 189% increase in articles mentioning substitutes to ozone-depleting substances; the DiD yields comparable estimates.  These results challenge the view that agreements foster technological diffusion without affecting much of the dynamics of innovation. Agreements can thus encourage the development of green technologies, which importantly suggests they should be negotiated as early as possible if we hope to solve global environmental problems.

Speaker: Melanie Revilla (RECSM Deputy Director, Universitat Pompeu Fabra)

Date: Thursday 24 October 2019, 3.15pm - 4.45pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)

Abstract: The expansion of the Internet and the development of a range of new active and passive measurement tools, particularly on mobile devices, present exciting opportunities for researchers, such as visual or voice data capture. Using these new measurement opportunities could reduce respondent burden, improve data quality, and extend measurement into new domains, allowing to answer questions that could not be answered so far and to improve the decisions of key actors such as governments.

While many people speak about these ideas, very little research has implemented such possibilities, and even less has assessed the data quality associated with such approaches. Thus, there is a huge need for further research in this area. In this presentation, I will discuss results from a few studies in which we started testing the use of three new measurement opportunities (voice input, visual data, and metered data) to improve data quality.

Speaker: Leke de Vries (Doctoral Candidate & National Institute of Justice Graduate Research Fellow, Northeastern University)

Date: Monday 29 April 2019, 12.30pm - 2.00pm

Room: Department of Methodology, Columbia House, LSE (COL.8.13)

Abstract: This talk presents how automated text analyses help illuminate relatively rare phenomena in large amounts of text. Human trafficking victimizations will be utilized as a case to illustrate the use of mathematical text analysis to reveal crime patterns and suggest new directions for research on crime or other complex problems. As has been noted in recent work, the application of supervised methods to detect and understand crime is challenged by lacking or biased ground truth. The examination of various crime patterns requires an inductive approach to the use of machine-learning techniques. The talk highlights the use of computational techniques based on distributional word and text representations to present indicators of crime-related activities. Distributional word and text representations have been used to detect other types of crimes for which limited or biased ground truth exists, including hate crime, abusive language, or gender biases. The presentation will focus on how patterns for rather infrequent words can be understood through models that weigh nearby-context words more heavily than distant words and suggests the use of qualitative methods to guide the model specification and interpretation.

Speaker: Dr Gah-Yi Ban (Assistant Professor of Management Science and Operations, London Business School)

Date: Thursday 28 March 2019, 2.30pm - 4.00pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)

Abstract: We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a d-dimensional feature vector. We assume a personalized demand model, parameters of which depend on s out of the d features. The seller initially does not know the relationship between the customer features and the product demand, but learns this through sales observations over a selling horizon of T periods. We prove that the seller’s expected regret, i.e., the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order s √T under any admissible policy. We then design a near-optimal pricing policy for a “semi-clairvoyant” seller (who knows which s of the d features are in the demand model) that achieves an expected regret of order s √Tlog T. We extend this policy to a more realistic setting where the seller does not know the true demand predictors, and show this policy has an expected regret of order s √T(log d＋logT), which is also near-optimal. Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods such as myopic pricing and segment-then-optimize policies. Furthermore, our policy improves upon the loan company’s historical pricing decisions by 32% in terms of annual expected revenue.

Speaker: Kevin Quinn (Professor, Department of Political Science, Univeristy of Michigan

Date: Wednesday 27 March 2019, 12.30pm - 2.00pm

Room: Department of Methodology, Columbia House, LSE (COL.8.13)

Abstract: Item response theory (IRT) models are widely used by political scientists to measure latent concepts such as attitudes or ideology. Unlike the canonical psychometric applications, many applications in political science feature extremely sparse data as well as a lack of prior information about the sign of the discrimination parameters. This lack of information can result in a posterior density with multiple modes—even when conventional identification assumptions are employed. In such situations, standard model-fitting strategies based on Gibbs sampling and data augmentation do a poor job of exploring all high probability areas of the parameter space. We develop a solution to this problem based on the idea of parallel tempering. We illustrate the approach with examples drawn from political science.

Speaker: Dr. Marc Kueschnigg (Associate Professor/Deputy Director, Institute for Analytical Sociology, Linköping University)

Date: Thursday 14 March 2019, 2.30pm - 4.00pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)

Abstract: The wide availability of online data marks a new era of investigation into human behavior embedded in social environments, and it allows us to change the focus of the analysis from that of the individual-level correlates of human behavior to the dynamics of populations of interacting individuals. Scrutinizing cultural choice at Spotify.com, a leading online music platform, we identify peer effects of social contagion among interconnected consumers of music to gain understanding of the mechanisms underlying the emergence of hits, the establishment of new artists and genres, and cultural change more generally. We utilize a massive networked dataset on music preferences and listening patterns collected through the Spotify API to estimate peer influence in the adoption of novel music. A granular measurement of music taste permits high-dimensional matching of users who either have or have not been exposed to new music through their network contacts. To overcome the selection bias typical for observational studies relying on statistical matching, we estimate treatment effects considering thousands of individual and contextual pre-treatment covariates which we measure and pre-process through unsupervised machine learning. This novel approach permits us to consider innovation characteristics, status differences between influencers and imitators, and treatment dosages moderating the strength of peer influence. Most importantly, our design allows us to determine whether peer influence merely informs people about current trends or whether it is truly persuasive---i.e. forceful enough to change people’s behavior and thus bring about surprising aggregate outcomes that do not map the distribution of individual preferences in a population but lead to the diffusion of the “unexpectable” resulting from path-dependent processes that could have produced a very different reality under similar circumstances.

Speaker: Nello Cristianini (Professor of Artificial Intelligence, University of Bristol)

Date: Thursday 28 February 2019, 2.30pm - 4.00pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)

Abstract: The way we build AI agents is based on machine learning and vast masses of training data, which is often the result of human activities.The consequences of this methodology are manifold, from privacy concerns to issues relative to implicit bias and discrimination. The business model underlying much of AI today is based on personalisation of services, which - for some - can generate concerns of personal data collection, possible manipulation of behaviour, and unexplored questions about dynamic pricing, opinion polarisation and possibly addiction. Understanding the technical and business models behind modern AI helps us understand the various challenges we face, devise regulation, and also consider possible negative effects of new technologies once deployed in society.

Speaker: Aaron R. Kaufman (PhD Candidate, Harvard University)

Date: Thursday 7 February 2019, 2.30pm - 4.00pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)

Abstract: Matching for causal inference is a well-studied problem, but standard methods fail when the units to match are text documents: the high-dimensional and rich nature of the data renders exact matching infeasible, causes propensity scores to produce incomparable matches, and makes assessing match quality difficult. In this talk, Aaron Kaufman characterizes a framework for matching text documents that decomposes existing methods into: (1) the choice of text representation, and (2) the choice of distance metric, investigate how different choices within this framework affect both the quantity and quality of matches identified through a systematic multifactor evaluation experiment using human subjects, and apply the results to the study of media bias.

Speaker: Dr Yunxiao Chen (Assistant Professor, Department of Statistics, LSE)

Date: Thursday 7 February 2019, 2.30pm - 4.00pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)

Abstract: Multidimensional unfolding methods are widely used for visualizing item response data. Such methods project respondents and items simultaneously onto a low-dimensional space, in which respondents and items are represented by ideal points with person-person, item-item, and person-item similarities being captured by the Euclidian distances between the points. In this paper, we study the visualization of multidimensional unfolding from a statistical perspective. We cast multidimensional unfolding into an estimation problem, where the respondent and item ideal points are treated as parameters to estimate. An estimator is then proposed for the simultaneous estimation of these parameters. An asymptotic theory and non-asymptotic error bounds are provided for the recovery of the ideal points, shedding lights on the validity of model-based visualization. An alternating projected gradient descent algorithm is proposed for parameter estimation. We provide two illustrative examples of users' movie rating and of senate roll call voting. (This is a joint project with Dr. Zhiliang Ying and Mr. Haoran Zhang).

Speaker: Dr Viktoria Spaiser (University Academic Fellow in Political Science Informatics, University of Leeds)

Date: Thursday 13 December 2018, 4.15pm - 5.45pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)

Abstract: In August 2014 the UN established an Independent Expert Advisory Group to make concrete recommendations on bringing about a data revolution in sustainable development. The hope has been that data analytics would help to deal with the enormous challenge of achieving a sustainable development globally. But what does existing data actually tell us about the challenge and potential solutions? And what other data do we need in order to understand the problem in all its dimensions? Dr Viktoria Spaiser will focus on the global and the individual level of the sustainability challenge. She will discuss recent studies that she conducted with colleagues modelling the compatibility of the UN Sustainable Development Goals on the one hand and studying environmental behaviour in a field-experimental setup on the other hand. Looking globally into the empirically measurable conflict of various Sustainable Development Goals, Dr Spaiser will explain what cross-country time-series data tells us about the nature of the inconsistencies. In this context she will also discuss a recent extension of the original study, examining the different conclusions that can be drawn about the sustainability challenge depending on how the Sustainable Development Goals are operationalized. Specifically, she will show why it does matter whether we look at production-based or consumption-based CO2 emissions when pursuing the Sustainable Development Agenda. Dr Spaiser will then change the perspective and look at the sustainability challenge from an individual level angle. The core question here is: how can we encourage more pro-environmental behaviour? She will discuss a pilot study conducted recently, making use of smartphones to collect daily environmental behaviour data in a field-experimental setup. Dr Spaiser will conclude with a programmatic note on the road ahead in the sustainability research she is envisioning.

Speaker: Professor Chris Bail (Professor of Sociology and Public Policy, Duke University)

Date: Wednesday 28 November 2018, 12.30pm - 2.00pm

Room: Department of Methodology, Columbia House, LSE (COL.8.13)

Abstract: There is mounting concern that social media sites contribute to political polarization by creating “echo chambers” that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 month that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science.

Speaker: Professor Gilat Levy & Professor Ronny Razin (Department of Economics, LSE)

Date: Thursday 15 November 2018, 4.15pm - 5.45pm

Room: Leverhulme Library, Columbia House, LSE (COL.6.15)