Morgan R. Frank Homepage

Upcoming Presentations:

NetSci 2024
IC2S2 2024
The AI and the Future of Work Conference at the Wharton School

Papers in Submission:

Attitudes Towards AI Transparency in Taiwan and the USA
Brian Porter, Morgan Frank, Yu-Ru Lin, and Edouard Macher.
Under review at Nature Human Behaviour.
Upward mobility in the green sector for frontline fossil fuel workers
Qing Chang, Michael Aklin, Morgan R. Frank, Shanti Gamper-Rabindran.
In preparation.
International brain-drain and and the impact of women empowerment
Alireza Javadian Sabet, Yixiao Li, Junghyun Lim, Morgan R. Frank.
In preparation.
From Workers' Exposure to Actionable Predictions of AI’s Impact on Work
Morgan R. Frank, Daniel Rock.
In preparation.
Pricing Job Priorities in Coal Communities
Junghyun Lim, Arezou Farzaneh, Shanti Gamper-Rabindran, Morgan R. Frank, Michael Aklin.
Under review at Science.
Enhacing Labor markets' resilience in preparation for the Net-Zero Transition and New Technologies through complex systems
R. Maria del Rio Chanona, Morgan R. Frank, Penny Mealy, Esteban Moro, Ljubica Nedelkoska.
Under review for a Santa Fe Institute edited volumes.
Causal analysis of stakeholder interdependencies in 10-k reports
Ivy Luo, Morgan R. Frank, Sandro Claudio Lera.
Under review at the Journal of Business Ethics (BUSI) (preprint).

Published Papers:

AI exposure predicts unemployment risk: A new approach to technology-driven job loss PNAS Nexus (2025)
Morgan R. Frank, Yong-Yeol 'YY' Ahn, Esteban Moro
Abstract: Is artificial intelligence (AI) disrupting jobs and creating unemployment? This question has stirred public concern for job stability and motivated studies assessing occupations’ automation risk. These studies used readily-available employment and wage statistics to quantify occupational changes for employed workers. However, they did not directly examine unemployment dynamics primarily due to the lack of data across occupations, geography, and time. Here, we overcome this barrier using monthly occupation-level unemployment data from each US state’s unemployment insurance office from 2010 to 2020 to assess AI exposure models, job separations, and unemployment through a new measure called unemployment risk. We demonstrate that standard employment statistics are inadequate proxies for occupations’ unemployment risk and find that individual AI exposure models are poor predictors of occupations’ unemployment risk states’ total unemployment rates, and states’ total job separation rates. However, an ensemble approach exhibits substantial predictive power, accounting for an additional 18% of variation in unemployment risk across occupations, states, and time compared to a baseline model that controls for education, occupations’ skills, seasonality, and regional effects. These results suggest that competing models may capture different aspects of AI exposure and that automation shapes US unemployment. Our results demonstrate the power of occupation-specific job disruption data and that efforts using only one AI exposure score will misrepresent AI’s impact on the future of work.

Behavior-based dependency networks between places shape urban economic resilience

Nature Human Behaviour (2024)

Takahiro Yabe, Bernardo Garcia Bulle Bueno, Morgan R. Frank, Alex Pentland, and Esteban Moro

Companion Website

A national longitudinal dataset of skills taught in U.S. higher education curricula Nature Scientific Data (2024)
Alireza Javadian Sabet, Sarah Bana, Renzhe Yu, Morgan R. Frank
Abstract: Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce. While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity. Here, we fill this gap by presenting Course-Skill Atlas – a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college education’s role in preparing students for the labor market. Overall, Course-Skill Atlas can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands, especially in the face of new technologies.

Network constraints on career mobility

Nature Cities (2024)

Morgan R. Frank, Esteban Moro, Tobin South, Alex Rutherford, Alex 'Sandy' Pentland, Bledi Taska, Iyad Rahwan

(preprint)

Location is a major barrier for transferring US fossil fuel employment to green jobs Nature Communications (2023)
Junghyun Lim, Michael Aklin, Morgan R. Frank
Abstract: The green energy revolution may displace 1.7 million fossil fuel workers in the US but a Just Transition to emerging green industry jobs offers possibilities for re-employing these workers. Here, using 14 years of power plant data from the US Energy Information Administration, job transition data from the Census Bureau, as well as employment and skills data from the Bureau of Labor Statistics, we assess whether people employed in fossil fuel resource extraction today are co-located and have the transferable skills to switch to expected green jobs. We find that these workers could leverage their mobility to other industries and have similar skills to green occupations. However, today’s fossil fuel extraction workers are not co-located with current sources of green energy production. Further, after accounting for federal employment projections, fossil fuel extraction workers are mostly not located in the regions where green employment will grow despite attaining the appropriate skillsets. These results suggest a large barrier to a Just Transition since fossil fuel extraction workers have not historically exhibited geospatial mobility. While stakeholders focus on re-skilling fossil fuel extraction workers, this analysis shows that co-location with emerging green employment will be the larger barrier to a Just Transition.

Allotaxonometry and rank-turbulence divergence: a universal instrument for comparing complex systems EPJ Data Science (2023)
Peter Sheridan Dodds, Joshua R. Minot, Michael V. Arnold, Thayer Alshaabi, Jane Lydia Adams, David Rushing Dewhurst, Tyler J. Gray, Morgan R. Frank, Andrew J. Reagan, and Christopher M. Danforth
Abstract: Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.

Transformative potential of AI: innovation, impact, and implications for the future workforce Santander X Xpert (2023)
Esteban Moro, Morgan R. Frank
Abstract: The paper discusses the transformative potential of generative AI and its impact on the future workforce. The authors suggest that while exposure to AI does not necessarily lead to employment loss, workers in certain occupations will have to adapt the most to generative AI. The study also highlights that exposure to generative AI is an equally urban and rural phenomenon, while exposure to AI language modeling is more abundant in urban settings. The authors recommend that businesses should strive to incorporate AI into their daily processes and explore innovative ways to improve productivity and drive growth. Overall, the paper emphasizes the need for further research into how occupations adapt to generative AI and the implications for the future workforce.

Connecting Higher Education to Workplace Activities and Earnings PLOS One (2023)
Hung Chau, Sarah H. Bana, Baptiste Bouvier, Morgan R. Frank
Abstract: Higher education is a source of skill acquisition for many middle- and high-skilled jobs. But what specific skills do universities impart on students to prepare them for desirable careers? In this study, we analyze a large novel corpora of over one million syllabi from over eight hundred bachelors’ granting US educational institutions to connect material taught in higher education to the detailed work activities in the US economy as reported by the US Department of Labor. First, we show how differences in taught skills both within and between college majors correspond to earnings differences of recent graduates. Further, we use the co-occurrence of taught skills across all of academia to predict the skills that will be taught in a major moving forward. Our unified information system connecting workplace skills to the skills taught during higher education can improve the workforce development of high-skilled workers, inform educational programs of future trends, and enable employers to quantify the skills of potential workers.

Art and the science of generative AI

Science (2023)

Ziv Epstein, Aaron Hertzmann, Memo Akten, Hany Farid, Jessica Fjeld, Morgan R. Frank, Matthew Groh, Laura Herman, Neil Leach, Robert Mahari, Alex ``Sandy'' Pentland, Olga Russakovsky, Hope Schroeder, Amy Smith

Image created by co-author Memo Atken.

Complete white paper here and companion website.

Driving Next to Automated Vehicles: Emergent Human-machine Cooperation in Mixed Traffic CHI (2023)
Yutong Zhang, Shihong Ling, Edmond Awad, Morgan R. Frank, Na Du
Abstract: Introducing automated vehicles (AVs) on public roads may challenge established norms as drivers in human-driven vehicles (HVs) learn to interact with AVs. Our study utilizes a game theory framework to investigate how driver and vehicle driving styles (aggressive vs. conservative), interaction types (HV-HV vs. HV-AV), scenario types (chicken game vs. public goods game scenarios), and time constraints (high vs. low) influence human drivers’ decision-making in mixed-traffic environments. According to an online survey study, drivers with aggressive driving styles and high time constraints were more likely to take aggressive actions. More importantly, there were significant interaction effects between vehicle driving styles and scenario types, between scenario types and time constraints, and between driver driving styles and interaction types on driver decision-making. Our findings provide essential insights into the design of AVs and promote the development of related laws and policies to facilitate human-machine cooperation in mixed-traffic environments.

Automation Impacts on China's Polarized Job Market Journal of Computational Social Science (2022)
Haohui Caron Chen, Xun Li, Morgan R. Frank, Xiaozhen Qin, Weipan Xu, Manuel Cebrian, Iyad Rahwan
Abstract: When facing automation threats, a large Chinese city worker might not be as lucky as a worker in a large US city. Empirical studies found that large US cities exhibit resilience to automation impacts because of the increased occupational and skill specialization. However, in this study, we observe polarized responses in large Chinese cities to automation impacts. The polarization might be attributed to the elaborate master planning of the central government. Cities are assigned with different industrial goals to achieve globally optimal economic success and a fast-growing economy. By dividing Chinese cities into two groups based on their administrative levels and premium resources allocated by the central government, we find that Chinese cities follow two distinct industrial development trajectories: one trajectory involves government support leading to a diversified industrial structure and, thus, a diversified job market, and in the other trajectory an absence of government support leads to specialty cities and, therefore, a specialized job market. By estimating the impact of automation on a polarized job market, we observe a Simpson's paradox: overall, there is not a statistically significant relationship between city size and resilience to automation, but when we disaggregate by the level of job diversity, we find larger cities with a diversified job market exhibit greater resilience, while larger cities with specialized job markets are more susceptible. These findings inform policymakers to deploy appropriate policies to mitigate polarized automation impacts.

Housing Prices and the Skills Composition of Neighborhoods Frontiers in Big Data (2021)
Shahad Althobaiti, Saud Alghumayjan, Morgan R. Frank, Esteban Moro, Ahmad Alabdulkareem, Alex 'Sandy' Pentland
Abstract: In the United States (US), low-income workers are being pushed away from city centers where the cost of living is high. The effects of such changes on labor mobility and housing price have been explored in the literature. However, few studies have focused on the occupations and specific skills that identify the most susceptible workers. For example, it has become increasingly challenging to fill the service sector jobs in the San Francisco (SF) Bay Area because appropriately skilled workers cannot afford the growing cost of living within commuting distance. With this example in mind, how does a neighborhood's skill composition change as a result of higher housing prices? Are there certain skill sets that are being pushed to the geographical periphery of a city despite their essentialness to the city's economy? Our study focuses on the impact of housing prices with a granular view of skills compositions to answer the following question: Has the density of cognitive skill workers been increasing in a gentrified area? We hypothesize that, over time, low-skilled workers are pushed away from downtown or areas where high-skill establishments thrive. Our preliminary results show that high-level cognitive skills are getting closer to the city center indicating adaptation to the increase of median housing prices as opposed to low-level physical skills that got further away. We examined tracts that the literature indicates as gentrified areas and found a pattern in which there is a temporal increase in median housing prices and the number of business establishments coupled with an increase in the percentage of skilled cognitive workers.

Universal resilience patterns in labor markets Nature Communications (2021)
Esteban Moro, Morgan R. Frank, Alex 'Sandy' Pentland, Alex Rutherford, Manuel Cebrian, Iyad Rahwan (*authors contributed equally)
Abstract: Cities are the innovation centers of the US economy, but technological disruptions can exclude workers and inhibit a middle class. Therefore, urban policy must promote the jobs and skills that increase worker pay, create employment, and foster economic resilience. In this paper, we model labor market resilience with an ecologically-inspired job network constructed from the similarity of occupations’ skill requirements. This framework reveals that the economic resilience of cities is universally and uniquely determined by the connectivity within a city’s job network. US cities with greater job connectivity experienced lower unemployment during the Great Recession. Further, cities that increase their job connectivity see increasing wage bills, and workers of embedded occupations enjoy higher wages than their peers elsewhere. Finally, we show how job connectivity may clarify the augmenting and deleterious impact of automation in US cities. Policies that promote labor connectivity may grow labor markets and promote economic resilience.

Developing China's Workforce Skill Taxonomy Reveals Extent of Labor Market Polarization Humanities and Social Sciences Communications (2021)
Weipan Xu, Xiaozhen Qin, Xun Li, Haohui Chen, Morgan R. Frank, Alex Rutherford, Andrew Reeson, Iyad Rahwan
Abstract: China, the world's second largest economy, is transitioning into an advanced, knowledge-based economy after four decades of rapid economic development. However, China still lacks a detailed understanding of the skills that underly the Chinese labor force, and the development and spatial distribution of these skills. Similar data has proven essential in other contexts; for example, the US standardized skill taxonomy, Occupational Information Network (ONET), played an important role in understanding the dynamics of manufacturing and knowledge-based work, and the potential risks from automation and outsourcing. Here, we use Machine Learning techniques to bridge this gap, creating China's first workforce skill taxonomy, and map it to ONET. This enables us to reveal workforce skill polarization into social-cognitive skills and sensory-physical skills, and to explore China's regional inequality in light of workforce skills, and compare it to traditional metrics such as education. We build an online tool for the public and policy makers to explore the skill taxonomy: skills.sysu.edu.cn. We also make the taxonomy dataset publicly available for other researchers.

Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts EPJ Data Science (2021)
Ryan J. Gallagher, Morgan R. Frank, Lewis Mitchell, Aaron J. Schwartz, Andrew J. Reagan, Christopher M. Danforth, Peter Sheridan Dodds
Abstract: A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content. However, collapsing the texts’ rich stories into a single number is often conceptually perilous, and it is difficult to confidently interpret interesting or unexpected textual patterns without looming concerns about data artifacts or measurement validity. To better capture fine-grained differences between texts, we introduce generalized word shift graphs, visualizations which yield a meaningful and interpretable summary of how individual words contribute to the variation between two texts for any measure that can be formulated as a weighted average. We show that this framework naturally encompasses many of the most commonly used approaches for comparing texts, including relative frequencies, dictionary scores, and entropy-based measures like the Kullback–Leibler and Jensen–Shannon divergences. Through a diverse set of case studies ranging from presidential speeches to tweets posted in urban green spaces, we demonstrate how generalized word shift graphs can be flexibly applied across domains for diagnostic investigation, hypothesis generation, and substantive interpretation. By providing a detailed lens into textual shifts between corpora, generalized word shift graphs help computational social scientists, digital humanists, and other text analysis practitioners fashion more robust scientific narratives.

A common trajectory recapitulated by urban economies Science Advances (2020)
Inho Hong, Morgan R. Frank, Iyad Rahwan, Woo-Sung Jung, Hyejin Youn
Abstract: Is there a universal economic pathway individual cities recapitulate over and over? This structure any evolutionaryifwould inform a reference model for fairer assessment, better maintenance, and improved forecasting of urban development. Using employment data including more than 100 million U.S. workers in all industries between 1998 and 2013, we empirically show that individual cities indeed recapitulate a common pathway where a transition to innovative economies is observed at the population of 1.2 million. This critical population is analytically derived by expressing the urban industrial structure as a function of scaling relations such that cities are divided into two economic categories: small city economies with sublinear industries and large city economies with superlinear industries. Last, we define a recapitulation score as an agreement between the longitudinal and the cross-sectional scaling exponents and find that nontradeable industries tend to adhere to the universal pathway more than the tradeable.

Towards Understanding the Impact of AI on Labor Proceedings of the National Academy of Science (2019)
Morgan R. Frank, David Autor, James E. Bessen, Erik Brynjolfsson, Manuel Cebrian, David J. Deming, Maryann Feldman, Matthew Groh, Jose Lobo, Esteban Moro, Dashun Wang, Hyejin Youn, Iyad Rahwan
Abstract: Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

The evolution of citation graphs in artificial intelligence research

Nature Machine Intelligence (2019)

Morgan R. Frank, Dashun Wang, Manuel Cebrian, Iyad Rahwan

(preprint)

Unpacking the polarization of workplace skills

Science Advances (2018)

Ahmad Alabdulkareem*, Morgan R. Frank*, Lijun Sun, Bedoor AlShebli, Cesar Hidalgo, Iyad Rahwan
(*author contributed equally)

Blog post.
Interactive website.

Small cities face greater impact from automation Journal of the Royal Society Interface (2018)
Morgan R. Frank, Lijun Sun, Manuel Cebrian, Hyejin Youn, Iyad Rahwan
Abstract: The city has proved to be the most successful form of human agglomeration and provides wide employment opportunities for its dwellers. As advances in robotics and artificial intelligence revive concerns about the impact of automation on jobs, a question looms: how will automation affect employment in cities? Here, we provide a comparative picture of the impact of automation across US urban areas. Small cities will undertake greater adjustments, such as worker displacement and job content substitutions. We demonstrate that large cities exhibit increased occupational and skill specialization due to increased abundance of managerial and technical professions. These occupations are not easily automatable, and, thus, reduce the potential impact of automation in large cities. Our results pass several robustness checks including potential errors in the estimation of occupational automation and subsampling of occupations. Our study provides the first empirical law connecting two societal forces: urban agglomeration and automation's impact on employment.

Detecting reciprocity at a global scale Science Advances (2018)
Morgan R. Frank, Nick Obradovich, Lijun Sun, Wei Lee Woon, Brad L. LeVeck, Iyad Rahwan
Abstract: Reciprocity stabilizes cooperation from the level of microbes all the way up to humans interacting in small groups, but does reciprocity also underlie stable cooperation between larger human agglomerations, such as nation states? Famously, evolutionary models show that reciprocity could emerge as a widespread strategy for achieving international cooperation. However, existing studies have only detected reciprocity-driven cooperation in a small number of country pairs. We apply a new method for detecting mutual influence in dynamical systems to a new large-scale data set that records state interactions with high temporal resolution. Doing so, we detect reciprocity between many country pairs in the international system and find that these reciprocating country pairs exhibit qualitatively different cooperative dynamics when compared to nonreciprocating pairs. Consistent with evolutionary theories of cooperation, reciprocating country pairs exhibit higher levels of stable cooperation and are more likely to punish instances of noncooperation. However, countries in reciprocity-based relationships are also quicker to forgive single acts of noncooperation by eventually returning to previous levels of mutual cooperation. By contrast, nonreciprocating pairs are more likely to exploit each other’s cooperation via higher rates of defection. Together, these findings provide the strongest evidence to date that reciprocity is a widespread mechanism for achieving international cooperation.

Validating Bayesian Truth-Serum in Large-scale Online Human Experiments PLOS ONE (2017)
Morgan R. Frank, Manuel Cebrian, Galen Pickard, Iyad Rahwan
Abstract: Bayesian truth serum (BTS) is an exciting new method for improving honesty and information quality in multiple-choice survey, but, despite the method’s mathematical reliance on large sample sizes, existing literature about BTS only focuses on small experiments. Combined with the prevalence of online survey platforms, such as Amazon’s Mechanical Turk, which facilitate surveys with hundreds or thousands of participants, BTS must be effective in large-scale experiments for BTS to become a readily accepted tool in real-world applications. We demonstrate that BTS quantifiably improves honesty in large-scale online surveys where the “honest” distribution of answers is known in expectation on aggregate. Furthermore, we explore a marketing application where “honest” answers cannot be known, but find that BTS treatment impacts the resulting distributions of answers.

The Lexicocalorimeter: Gauging public health through caloric input and output on social media

PLOS ONE (2017)

Sharon E. Alajajian, Jake Ryland Williams, Andrew J. Reagan, Stephen C. Alajajian, Morgan R. Frank, Lewis Mitchell, Jacob Lahne, Christopher M. Danforth, Peter Sheridan Dodds

Interactive Website.

Human language reveals a universal positivity bias Proceedings of the National Academy of Science (2015)
Peter Sheridan Dodds, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, Christopher M. Danforth
Abstract: Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (i) the words of natural human language possess a universal positivity bias, (ii) the estimated emotional content of words is consistent between languages under translation, and (iii) this positivity bias is strongly independent of frequency of word use. Alongside these general regularities, we describe interlanguage variations in the emotional spectrum of languages that allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts.

Standing swells surveyed showing surprisingly stable solutions to the Lorenz '96 Model

International Journal of Bifurcations and Chaos (2014)

Morgan R. Frank, Lewis Mitchell, Peter Sheridan Dodds, Christopher M. Danforth

Example Video.

An Evolutionary Algorithm approach to link prediction in dynamic social networks Journal of Computational Science (2014)
Catherine A. Bliss, Morgan R. Frank, Christopher M. Danforth, Peter Sheridan Dodds
Abstract: Many real world, complex phenomena have underlying structures of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics, with a key test being the prediction of short and long term changes. For the problem of short-term link prediction, existing methods attempt to determine neighborhood metrics that correlate with the appearance of a link in the next observation period. Recent work has suggested that the incorporation of topological features and node attributes can improve link prediction. We provide an approach to predicting future links by applying the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are used in a linear combination of sixteen neighborhood and node similarity indices. We examine a large dynamic social network with over 106 nodes (Twitter reciprocal reply networks), both as a test of our general method and as a problem of scientific interest in itself. Our method exhibits fast convergence and high levels of precision for the top twenty predicted links. Based on our findings, we suggest possible factors which may be driving the evolution of Twitter reciprocal reply networks.

Happiness and the patterns of life: a study of geolocated tweets Nature Scientific Reports (2013)
Morgan R. Frank, Lewis Mitchell, Peter Sheridan Dodds, Christopher M. Danforth
Abstract: The patterns of life exhibited by large populations have been described and modeled both as a basic science exercise and for a range of applied goals such as reducing automotive congestion, improving disaster response, and even predicting the location of individuals. However, these studies have had limited access to conversation content, rendering changes in expression as a function of movement invisible. In addition, they typically use the communication between a mobile phone and its nearest antenna tower to infer position, limiting the spatial resolution of the data to the geographical region serviced by each cellphone tower. We use a collection of 37 million geolocated tweets to characterize the movement patterns of 180,000 individuals, taking advantage of several orders of magnitude of increased spatial accuracy relative to previous work. Employing the recently developed sentiment analysis instrument known as the hedonometer, we characterize changes in word usage as a function of movement, and find that expressed happiness increases logarithmically with distance from an individual's average location.

THE GEOGRAPHY OF HAPPINESS: CONNECTING TWITTER SENTIMENT AND EXPRESSION, DEMOGRAPHICS, AND OBJECTIVE CHARACTERISTICS OF PLACE PLOS ONE (2013)
Lewis Mitchell, Morgan R. Frank, Kameron Decker Harris, Peter Sheridan Dodds, Christopher M. Danforth
Abstract: We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated in 2011 on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates.

Enumerating costas latin squares Journal of Combinatorial Mathematics and Combinatorial Computation (2012)
Morgan R. Frank, Jeffery Dinitz
Abstract: A Costas array of order n is an n×n permutation matrix with the property that all of the n(n−1)/2 line segments between pairs of 1’s differ in length or in slope. A Costas latin square of order n is an n × n latin square where for each symbol k, with 1 ≤ k ≤ n, the cells containing k determine a Costas array. The existence of a Costas latin square of side n is equivalent to the existence of n mutually disjoint Costas arrays. In 2012, Dinitz, Ostergard and Stinson enumerated all Costas latin squares of side n ≤ 27. In this brief note, a sequel to that paper, we extend this search to sides n = 28 and 29. In addition we determine the sizes of maximal sets of disjoint Costas latin Squares of side n for n ≤ 29.

Preprints:

Longitudinal Complex Dynamics of Labour Markets Reveal Increasing Polarisation (preprint, 2022)
Shahad Althobaiti, Ahmad Alabdulkareem, Judy Hanwen Shen, Iyad Rahwan, Morgan R. Frank, Esteban Moro, Alex Rutherford
Abstract: In this paper we conduct a longitudinal analysis of the structure of labour markets in the US over 7 decades of technological, economic and policy change. We make use of network science, natural language processing and machine learning to uncover structural changes in the labour market over time. We find a steady rate of both disappearance of jobs and a shift in the required work tasks, despite much technological and economic change over this time period. Machine learning is used to classify jobs as being predominantly cognitive or physical based on the textual description of the workplace tasks. We also measure increasing polarisation between these two classes of jobs, linked by the similarity of tasks, over time that could constrain workers wishing to move to different jobs.

Exposure of occupations to technologies of the fourth industrial revolution (preprint, 2021)
Benjamin Meindl, Morgan R. Frank, Joana Mendonca
Abstract: The fourth industrial revolution (4IR) is likely to have a substantial impact on the economy. Companies need to build up capabilities to implement new technologies, and automation may make some occupations obsolete. However, where, when, and how the change will happen remain to be determined. Robust empirical indicators of technological progress linked to occupations can help to illuminate this change. With this aim, we provide such an indicator based on patent data. Using natural language processing, we calculate patent exposure scores for more than 900 occupations, which represent the technological progress related to them. To provide a lens on the impact of the 4IR, we differentiate between traditional and 4IR patent exposure. Our method differs from previous approaches in that it both accounts for the diversity of task-level patent exposures within an occupation and reflects work activities more accurately. We find that exposure to 4IR patents differs from traditional patent exposure. Manual tasks, and accordingly occupations such as construction and production, are exposed mainly to traditional (non-4IR) patents but have low exposure to 4IR patents. The analysis suggests that 4IR technologies may have a negative impact on job growth; this impact appears 10 to 20 years after patent filing. Further, we compared the 4IR exposure to other automation and AI exposure scores. Whereas many measures refer to theoretical automation potential, our patent-based indicator reflects actual technology diffusion. Our work not only allows analyses of the impact of 4IR technologies as a whole, but also provides exposure scores for more than 300 technology fields, such as AI and smart office technologies. Finally, the work provides a general mapping of patents to tasks and occupations, which enables future researchers to construct individual exposure measures.

Industrial Topics in Urban Labor System (preprint, 2020)
Jaehyuk Park, Morgan R. Frank, Lijun Sun, Hyejin Youn
Abstract: Categorization is an essential component for us to understand the world for ourselves and to communicate it collectively. It is therefore important to recognize that classification system are not necessarily static, especially for economic systems, and even more so in urban areas where most innovation takes place and is implemented. Out-of-date classification systems would potentially limit further understanding of the current economy because things constantly change. Here, we develop an occupation-based classification system for the US labor economy, called industrial topics, that satisfy adaptability and representability. By leveraging the distributions of occupations across the US urban areas, we identify industrial topics - clusters of occupations based on their co-existence pattern. Industrial topics indicate the mechanisms under the systematic allocation of different occupations. Considering the densely connected occupations as an industrial topic, our approach characterizes regional economies by their topical composition. Unlike the existing survey-based top-down approach, our method provides timely information about the underlying structure of the regional economy, which is critical for policymakers and business leaders, especially in our fast-changing economy.

Constructing a taxonomy of fine-grained human movement and activity motifs through social media (preprint, 2014)
Morgan R. Frank, Jake Ryland Williams, Lewis Mitchell, James P. Bagrow, Peter Sheridan Dodds, Christopher M. Danforth
Abstract: Profiting from the emergence of web-scale social data sets, numerous recent studies have systematically explored human mobility patterns over large populations and large time scales. Relatively little attention, however, has been paid to mobility and activity over smaller time-scales, such as a day. Here, we use Twitter to identify people's frequently visited locations along with their likely activities as a function of time of day and day of week, capitalizing on both the content and geolocation of messages. We subsequently characterize people's transition pattern motifs and demonstrate that spatial information is encoded in word choice.

Shadow networks: Discovering hidden nodes with models of information flow (preprint, 2013)
James P. Bagrow, Suma Desu, Morgan R. Frank, Narine Manukyan, Lewis Mitchell, Andrew Reagan, Eric E. Bloedorn, Lashon B. Booker, Luther K. Branting, Michael J. Smith, Brian F. Tivnan, Christopher M. Danforth, Peter S. Dodds, Joshua C. Bongard
Abstract: Complex, dynamic networks underlie many systems, and understanding these networks is the concern of a great span of important scientific and engineering problems. Quantitative description is crucial for this understanding yet, due to a range of measurement problems, many real network datasets are incomplete. Here we explore how accidentally missing or deliberately hidden nodes may be detected in networks by the effect of their absence on predictions of the speed with which information flows through the network. We use Symbolic Regression (SR) to learn models relating information flow to network topology. These models show localized, systematic, and non-random discrepancies when applied to test networks with intentionally masked nodes, demonstrating the ability to detect the presence of missing nodes and where in the network those nodes are likely to reside.

Lecture for the S3D Computational Social Science Seminar at CMU	Fall 2018 Lecture at the Minneapolis Federal Reserve Bank
PhD Dissertation Defense at the MIT Media Lab (2019)	Interview with the Federal Reserve Bank of Boston (2019)
An interview with SAGE on my research methods

Morgan R. Frank

Overview of research for Imagination in Action conference 2019

The Social Complexity of Technological Change

Lecture for the S3D Computational Social Science Seminar at CMU

Fall 2018 Lecture at the Minneapolis Federal Reserve Bank

PhD Dissertation Defense at the MIT Media Lab (2019)

Interview with the Federal Reserve Bank of Boston (2019)

An interview with SAGE on my research methods

Attitudes Towards AI Transparency in Taiwan and the USA

Upward mobility in the green sector for frontline fossil fuel workers

International brain-drain and and the impact of women empowerment

From Workers' Exposure to Actionable Predictions of AI’s Impact on Work

Pricing Job Priorities in Coal Communities

Enhacing Labor markets' resilience in preparation for the Net-Zero Transition and New Technologies through complex systems

Causal analysis of stakeholder interdependencies in 10-k reports

Postdoctoral Associate at MIT's Media Lab

(June 2019-2020)

PhD at MIT's Media Lab

(2014-June 2019)

Master's degree at the University of Vermont

(2012-2014)

Bachelor's degree at the University of Vermont

(2008-2012)