![]() ![]() ![]() We describe our approach to machine learning as agnostic because we avoid assuming that the data emerge from a process that matches our machine learning method. This article provides an overview of how social scientists have used machine learning methods, how they have evaluated the performance of models, and what is distinctive about a social science approach to machine learning. Instead, we adopt a more inductive approach, which involves sequential and iterative inferences-a reality that characterizes much of social science work, but that is difficult to talk about because it conflicts with the dominant deductive framing of research. In this article, we argue that the current abundance of data allows us to break free from the deductive mindset that was previously necessitated by data scarcity. Likewise, the introduction of machine learning methods also invites us to reevaluate the typical model of social science. Unlocking this potential involves reconsidering the conventions of machine learning and reapplying these techniques to accomplish social science tasks such as discovery, measurement, and causal inference. Just as they have transformed so many other areas of life, machine learning methods have transformative potential in social science. The results include not only more accurate spam filters but also algorithms that can generate realistic fake images, write near-human-quality prose, and defeat world champion human players in games of strategy. By focusing on optimizing performance on such tasks, the community has made astonishingly rapid progress. This includes not only explicitly predictive tasks, such as classifying emails as spam or predicting who will click on an advertisement, but also other tasks amenable to quantitative feedback, such as compressing information in an image or maneuvering a robot in an environment. The machine learning community largely prioritizes performance on established quantitative benchmarks. Machine learning is a class of flexible algorithmic and statistical techniques for prediction and dimension reduction. Social scientists increasingly rely on machine learning methods to make the most of this new abundance. Computing power has also exploded, with personal computers able to analyze millions of rows of data and more powerful cloud computing services readily available. New forms of data can fundamentally change our ability to measure phenomena for example, tracking the removal of social media posts in real time provides a new window into how authoritarian regimes control information available to the public. The difference is not just a matter of scale. International relations scholars can bolster careful reading of archives with the analysis of millions of declassified state department cables. Election scholars used to rely on occasional surveys administered around national elections now, researchers use voter files with millions of records. The rapid expansion of available data has shifted the evidence base. The consequence of this scarcity was that social scientists developed and relied on statistical techniques that enabled progress with few data and even less computing power.Ībundance now defines the social sciences. Computation was an even more pressing bottleneck with limited and expensive computing time. Data were hard to find, surveys were costly to field, and record storage was close to impossible. Students are encouraged to pass at least two exams by the time they graduate.For much of its history, empirical work in the social sciences has been defined by scarcity. These exams are administered by the Society of Actuaries and the Casualty Actuarial Society. Our actuarial science program is designed to prepare you for the first four actuarial exams (P, FM, IFM and LTAM). News & World Report’s “2020 Best Jobs Ranking,” actuary ranked 11 th in best business jobs and ranked No. The actuarial employment rate is projected to grow 20 percent to 2028, and it is one of the fastest growing professions. These talents will strengthen your education, make you a well-rounded individual and enhance your appeal to employers worldwide. ![]() The study of actuarial science includes comprehensive training in thinking, reasoning, computing and problem-solving. You’ll have an impact on as business, industry, government, education and more. As an actuary professional, you’ll use your problem-solving expertise to make real contributions.
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