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Years ago, it became established that the severity and length of the Great Depression were due largely to misguided Federal Reserve monetary policy and the resulting catastrophic bank failures. This result is confirmed by surveys of scholars in the area and books specifically written on economic history. Yet the leading textbooks used in high school US history fail to reflect these findings. The texts (1) tend to ignore the recognized causes of the Great Depression's severity and (2) overstate the role of other causes rejected by the majority of economic historians. After laying out this curious disjuncture, this article presents curricular resources to help high school teachers more accurately present the Great Depression's causes.

Advances in Artificial Intelligence (AI) technology and related fields like robotics have made it possible that automated systems will reach and then exceed human performance on more and more tasks. The affected fields span the economy, from health and education to energy and the environment. In this article, the authors define AI and provide some historical context with emphasis on what economists have called "creative destruction."

BackgroundThe heterogeneity of breast cancer makes identifying effective therapies challenging. The I-SPY 2 trial, a multicenter, adaptive phase 2 trial of neoadjuvant therapy for high-risk clinical stage II or III breast cancer, evaluated multiple new agents added to standard chemotherapy to assess the effects on rates of pathological complete response (i.e., absence of residual cancer in the breast or lymph nodes at the time of surgery).MethodsWe used adaptive randomization to compare standard neoadjuvant chemotherapy plus the tyrosine kinase inhibitor neratinib with control. Eligible women were categorized according to eight biomarker subtypes on the basis of human epidermal growth factor receptor 2 (HER2) status, hormone-receptor status, and risk according to a 70-gene profile. Neratinib was evaluated against control with regard to 10 biomarker signatures (prospectively defined combinations of subtypes). The primary end point was pathological complete response. Volume changes on serial magnetic resonance imaging were used to assess the likelihood of such a response in each patient. Adaptive assignment to experimental groups within each disease subtype was based on Bayesian probabilities of the superiority of the treatment over control. Enrollment in the experimental group was stopped when the 85% Bayesian predictive probability of success in a confirmatory phase 3 trial of neoadjuvant therapy reached a prespecified threshold for any biomarker signature ("graduation"). Enrollment was stopped for futility if the probability fell to below 10% for every biomarker signature.ResultsNeratinib reached the prespecified efficacy threshold with regard to the HER2-positive, hormone-receptor-negative signature. Among patients with HER2-positive, hormone-receptor-negative cancer, the mean estimated rate of pathological complete response was 56% (95% Bayesian probability interval [PI], 37 to 73%) among 115 patients in the neratinib group, as compared with 33% among 78 controls (95% PI, 11 to 54%). The final predictive probability of success in phase 3 testing was 79%.ConclusionsNeratinib added to standard therapy was highly likely to result in higher rates of pathological complete response than standard chemotherapy with trastuzumab among patients with HER2-positive, hormone-receptor-negative breast cancer. (Funded by QuantumLeap Healthcare Collaborative and others; I-SPY 2 TRIAL ClinicalTrials.gov number, NCT01042379.).

BackgroundThe genetic and clinical heterogeneity of breast cancer makes the identification of effective therapies challenging. We designed I-SPY 2, a phase 2, multicenter, adaptively randomized trial to screen multiple experimental regimens in combination with standard neoadjuvant chemotherapy for breast cancer. The goal is to match experimental regimens with responding cancer subtypes. We report results for veliparib, a poly(ADP-ribose) polymerase (PARP) inhibitor, combined with carboplatin.MethodsIn this ongoing trial, women are eligible for participation if they have stage II or III breast cancer with a tumor 2.5 cm or larger in diameter; cancers are categorized into eight biomarker subtypes on the basis of status with regard to human epidermal growth factor receptor 2 (HER2), hormone receptors, and a 70-gene assay. Patients undergo adaptive randomization within each biomarker subtype to receive regimens that have better performance than the standard therapy. Regimens are evaluated within 10 biomarker signatures (i.e., prospectively defined combinations of biomarker subtypes). Veliparib-carboplatin plus standard therapy was considered for HER2-negative tumors and was therefore evaluated in 3 signatures. The primary end point is pathological complete response. Tumor volume changes measured by magnetic resonance imaging during treatment are used to predict whether a patient will have a pathological complete response. Regimens move on from phase 2 if and when they have a high Bayesian predictive probability of success in a subsequent phase 3 neoadjuvant trial within the biomarker signature in which they performed well.ResultsWith regard to triple-negative breast cancer, veliparib-carboplatin had an 88% predicted probability of success in a phase 3 trial. A total of 72 patients were randomly assigned to receive veliparib-carboplatin, and 44 patients were concurrently assigned to receive control therapy; at the completion of chemotherapy, the estimated rates of pathological complete response in the triple-negative population were 51% (95% Bayesian probability interval [PI], 36 to 66%) in the veliparib-carboplatin group versus 26% (95% PI, 9 to 43%) in the control group. The toxicity of veliparib-carboplatin was greater than that of the control.ConclusionsThe process used in our trial showed that veliparib-carboplatin added to standard therapy resulted in higher rates of pathological complete response than standard therapy alone specifically in triple-negative breast cancer. (Funded by the QuantumLeap Healthcare Collaborative and others; I-SPY 2 TRIAL ClinicalTrials.gov number, NCT01042379.).

The cryptocurrency Bitcoin has been prominently featured in the news recently. Its ascension in value has been nothing short of extraordinary. This article briefly explains what Bitcoin is and how it works. The more challenging question is what Bitcoin--this cryptographic breakthrough--really is: currency, like the U.S. dollar, an asset, more like gold, or something else? Further, can this astonishing run-up in value continue or will Bitcoin be added to the long list of so-called "asset bubbles" that eventually burst, causing pain for those that own them? The phenomenon of Bitcoin provides a good example for teaching about asset value, fundamental value and speculative bubbles.

This article systematically examines differences between student and faculty perspectives on quality instruction, using local data for a regional university's college of business matched to RateMyProfessors.com evaluations. Particular attention is focused on gender bias, "hotness," and "easiness." In addition to documenting a student bias or preference in favor of male faculty, this article also shows how students and faculty have different views of "easiness" and "hotness." For this college, students value "easiness" while faculty penalize it; however "hotness" has no measurable effect on faculty in this study.

Do students engage in offsetting behavior, adapting their study effort to the difficulty of learning? The authors present the results of survey research intended to test for the presence of offsetting behavior at a regional university. Instead of trying to determine whether students study less when learning is easier, we check to see whether students study more when learning is harder. Our results strongly indicate the presence of offsetting behavior, though the incidence of the effect varies systematically by gender, year in school, and major. The authors conclude with a discussion of implications for business educators.

In 1890, the United States government had no agency empowered to control the overall supply of money. Fifty years later, it had a full set of monetary institutions, including a central bank whose structure is much the same today. Further, it had enough experience to know both the promise and the pitfalls of monetary control. How did the nation's monetary institutions change so much? History books make the progression from the 1890s debates and early twentieth century crises look inevitable. Yet because there is little economic content, the textbooks do not explain monetary history within a contextual framework that includes choices, costs, and incentives. As in other areas, textbooks underplay the economic way of thinking. This article outlines the macroeconomic understandings required to help students navigate the complexities of a key period in American monetary history. We show how economic reasoning illuminates three episodes from that period--the hard currency debate of the 1890s, the panic of 1907, and the Great Depression of the 1930s.

"One who rides a tiger is afraid to dismount," says a colorful proverb from an earlier time. This may be an apt saying for the situation facing the new head of the Federal Reserve, Janet L. Yellen, who takes over at a time when successive rounds of Fed policy have taken the central bank into uncharted territory. By historical standards, Fed policy has been quite aggressive. Now that the time has arrived to unwind that policy, the withdrawal effects may be serious. Called "quantitative easing," the Fed's policy has involved huge injections into the banking system through repeated massive purchases of securities. Janet Yellen--the first woman to assume the post of chair of the Federal Reserve's Board of Governors--succeeds Ben S. Bernanke, who served as chair from 2006 to 2013. What is her macroeconomic economic philosophy? In her outlook, Yellen is generally Keynesian (a school of economic thought favoring government intervention to stabilize the economy, originally inspired by the British economist John Maynard Keynes). In her new role, her primary task is to put monetary policy on a more normal footing without endangering economic recovery. In this article, the author considers ways in which she might approach this task given the challenges involved.

"Bloomberg Businessweek" ranks U.S. undergraduate business programs annually. These rankings provide a convenient overall measure of quality, which is important in today's environment of concern about higher education costs and employment after graduation. Data envelopment analysis (DEA) has advantages over previous regression approaches in characterizing value added. The authors use a DEA approach to estimate relative efficiencies based on starting salaries and recruiter surveys, identifying some schools as overachievers relative to their "Bloomberg Businessweek" rankings. In particular, DEA-based reranking highlights the ability of some public institutions facing high student-faculty ratios to turn out well-regarded graduates with high starting salaries.