“The Art of the Probable” addresses the history of scientific ideas, in particular the emergence and development of mathematical probability. But it is neither meant to be a history of the exact sciences per se nor an annex to, say, the Course 6 curriculum in probability and statistics. Rather, our objective is to focus on the formal, thematic, and rhetorical features that imaginative literature shares with texts in the history of probability. These shared issues include (but are not limited to): the attempt to quantify or otherwise explain the presence of chance, risk, and contingency in everyday life; the deduction of causes for phenomena that are knowable only in their effects; and, above all, the question of what it means to think and act rationally in an uncertain world.
Our course therefore aims to broaden students’ appreciation for and understanding of how literature interacts with – both reflecting upon and contributing to – the scientific understanding of the world. We are just as centrally committed to encouraging students to regard imaginative literature as a unique contribution to knowledge in its own right, and to see literary works of art as objects that demand and richly repay close critical analysis. It is our hope that the course will serve students well if they elect to pursue further work in Literature or other discipline in SHASS, and also enrich or complement their understanding of probability and statistics in other scientific and engineering subjects they elect to take.

This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.

Biology is designed for multi-semester biology courses for science majors. It is grounded on an evolutionary basis and includes exciting features that highlight careers in the biological sciences and everyday applications of the concepts at hand. To meet the needs of today’s instructors and students, some content has been strategically condensed while maintaining the overall scope and coverage of traditional texts for this course. Instructors can customize the book, adapting it to the approach that works best in their classroom. Biology also includes an innovative art program that incorporates critical thinking and clicker questions to help students understand—and apply—key concepts.

By the end of this section, you will be able to:Discuss the concept of entropyExplain the first and second laws of thermodynamics

By the end of this section, you will be able to:Discuss the concept of entropyExplain the first and second laws of thermodynamics

Using a website simulation tool, students build on their understanding of random processes on networks to interact with the graph of a social network of individuals and simulate the spread of a disease. They decide which two individuals on the network are the best to vaccinate in an attempt to minimize the number of people infected and "curb the epidemic." Since the results are random, they run multiple simulations and compute the average number of infected individuals before analyzing the results and assessing the effectiveness of their vaccination strategies.

In this online activity, learners discover how random variation influences biological evolution. Biological evolution is often thought of as a process by which adaptation is generated through selection.Œć While it is recognized that random variation underlies the process, emphasis is usually placed on selection and resulting adaptation, leaving a sense that it is selection that drives evolution.Œć This simulation highlights the creative role of random variation, offering a somewhat different perspective: that of evolution as open-ended exploration driven by randomness and constrained by selection, with adaptation as a dynamic, transient consequence rather than an objective.

This course provides practical instruction in the design and analysis of non-digital games. Students cover the texts, tools, references and historical context to analyze and compare game designs across a variety of genres, including sports, game shows, games of chance, card games, schoolyard games, board games, and role–playing games. In teams, students design, develop, and thoroughly test their original games to understand the interaction and evolution of game rules. Students taking the graduate version complete additional assignments.

An historical examination and analysis of the evolution and development of games and game mechanics. Topics include a large breadth of genres and types of games, including sports, game shows, games of chance, schoolyard games, board games, roleplaying games, and digital games. Students submit essays documenting research and analysis of a variety of traditional and eclectic games. Project teams required to design, develop, and thoroughly test their original games.

Students learn about complex networks and how to use graphs to represent them. They also learn that graph theory is a useful part of mathematics for studying complex networks in diverse applications of science and engineering, including neural networks in the brain, biochemical reaction networks in cells, communication networks, such as the internet, and social networks. Students are also introduced to random processes on networks. An illustrative example shows how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students, and demonstrates how scientists and engineers use mathematics and computers to model and simulate random processes on complex networks for the purposes of learning more about our world and creating solutions to improve our health, happiness and safety.

This model-eliciting activity has students create rules to allow them to judge whether or not the shuffle feature on a particular iPod appears to produce randomly generated playlists. Because people's intuitions about random events and randomly generated data are often incorrect or misleading, this activity initially focuses students' attention on describing characteristics of 25 playlists that were randomly generated. Students then use these characteristics to come up with rules for judging whether a playlist does NOT appear to be randomly generated. Students test and revise their rules (model) using five additional playlsits. Then, they apply their model to three particular playlists that have been submitted to Apple by an unhappy iPod owner who claims the shuffle feature on his iPod is not generating random playlists. In the final part of the activity, students write a letter to the ipod owner, on behalf of Apple, explaining the use of their model and their final conclusion about whether these three suspicious playlists appear to have been randomly generated.This lesson provides an introduction to the fundamental ideas of randomness, random sequences and random samples.

Building on their understanding of graphs, students are introduced to random processes on networks. They walk through an illustrative example to see how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students. This demonstrates how scientists and engineers use mathematics to model and simulate random processes on complex networks. Topics covered include random processes and modeling disease spread, specifically the SIR (susceptible, infectious, resistant) model.