The actions on Friday restricting certain news organizations from a briefing by the White House Press Secretary raise significant concerns. The D.C. Circuit almost forty years ago held in no uncertain terms that access to White House press facilities cannot be arbitrarily denied to credentialed reporters. Courts routinely have held that government press briefings are the type of public forum to which press access may never be restricted based on objections to the content of a journalist’s reporting. The Supreme Court itself has made clear that, above all else, the First Amendment means that government has no power to restrict expression because of its message, its ideas, its subject matter, or its content. Friday’s actions appear to be a highly dangerous and improper effort to do just that.
Source: 9 Top First Amendment Experts React to White House Press Briefing Ban on CNN, NYT, others | Just Security
Africa often brings policy surprises. Two of the most significant political moments in Africa—the street demonstrations that toppled Compaoré in Burkina Faso, and the surprisingly free and fair elections in The Gambia that ended Jammeh’s rule—were wholly unanticipated. Smart and capable diplomats on the ground made all the difference in preventing either situation from barreling into a full-blown crisis. The right support and resources from Washington helped U.S. diplomats reinforce these unexpected democratic opportunities and facilitate peaceful transitions. U.S. efforts have made a crucial difference in Africa during both the George W. Bush and Barack Obama administrations. They saved lives, prevented mass atrocities, brokered peaceful solutions, and helped create the conditions for more prosperous futures. The new administration should continue this engagement.
Source: Why the Trump Administration Should Not Overlook Africa – Carnegie Endowment for International Peace
We examine how machine learning can be used to improve and understand human decision-making. In particular, we focus on a decision that has important policy consequences. Millions of times each year, judges must decide where defendants will await trial—at home or in jail. By law, this decision hinges on the judge’s prediction of what the defendant would do if released. This is a promising machine learning application because it is a concrete prediction task for which there is a large volume of data available. Yet comparing the algorithm to the judge proves complicated. First, the data are themselves generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the single variable that the algorithm focuses on; for instance, judges may care about racial inequities or about specific crimes (such as violent crimes) rather than just overall crime risk. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: a policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0% with no increase in crime rates. Moreover, we see reductions in all categories of crime, including violent ones. Importantly, such gains can be had while also significantly reducing the percentage of African-Americans and Hispanics in jail. We find similar results in a national dataset as well. In addition, by focusing the algorithm on predicting judges’ decisions, rather than defendant behavior, we gain some insight into decision-making: a key problem appears to be that judges to respond to ‘noise’ as if it were signal. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
Source: Human Decisions and Machine Predictions
An interesting thing happened on the way to implementing Sustaining Excellence. Our operating budget for FY17 inherited more challenges from the previous year than originally estimated. In FY16 we ended up with a $4.5 Million net operating loss — the first operating loss in over 20 years. If repeated, such a deficit would have a negative impact on the University’s financial rating. Think of it this way. When you manage your household finances, you thoughtfully plan a budget for the year by making careful estimates of your income and projected expenses. All goes well until something unexpected occurs. Maybe the transmission on your car falls out. Maybe the orthodontist announces that your child needs braces. Possibly your water heater explodes. You suddenly face an unforeseen bill that blows the budget, and you have to adjust your spending to cover the costs. In planning for our current year budget, FY 17, our original calculations of needs to operate the university was short $8.4 Million. We reworked this budget and balanced it by June 2016, with a reduced merit increase, a 5% reduction in operating expenses, and a small contingency. Believing all was well, in the Fall we discovered a reduced enrollment in certain graduate programs and an under-realization of gift funds held in various departments to support operations. Just like your household, we had to adjust. This meant an additional 1.25% reduction across all cabinet areas in operating expenses. This was not quite the perfect storm, but stormy enough. These cuts in operating funds have caused a number of our colleagues to question the university’s financial health. Some have even speculated that we face a financial crisis. Others wonder about the possibility of a further budget shortfall. For these reasons, the annual Budget Forum, one week from today, will be a particularly important opportunity to learn more and to ask questions. There you will hear a deeper explanation of the issues of cash flow, construction, pledged donations, and university debt capacity.
My few comments. (1) I hate analogies of budgets of large complex organizations to households. Such analogies are shrouded in an earnest language of “let me help you children understand” but the real intent is to obfuscate with a poor analogy. The intertemporal budget constraint (i.e. over time) of a large, complex organization is usually quite different from that of a household. (2) This sentence: “In planning for our current year budget, FY 17, our original calculations of needs to operate the university was short $8.4 Million.” “Needs” seems to be referring to “revenue flows.” That is, “Our projected revenue flows were $8.4m below projected expenses.” Odd choice of language. We “need” this money! (3) A 6.25% cut in total budget (he isn’t clear) amounts to about $28m. Pres. Engh attributes the shortfall to declining graduate enrollment, but let’s say graduate students pay $35,000 in tuition per year, then enrollment would have had to go down by 800 students (almost one third of total enrollments). Is that what happened? How did our leadership allow graduate enrollments to decline so precipitously? Where is the urgent task force to restore graduate enrollments?
Source: State of the University Address, 2017 – Public Addresses – Selected Writings – Office of the President – Santa Clara University