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The Neuroscience of Evidentiary Rules: The Case of the Present Sense Impression
No full textThe Federal Rules of Evidence (FRE) play a critical role in federal trials by determining what evidence the jury will be allowed to hear. Nonetheless, the rules are largely premised on untested psychological assumptions. The Present Sense Impression Rule (PSIR), for example, is an exception to the general ban against hearsay based on the assumptions that statements about contemporaneous events have fewer memory errors; are less likely to be lies; and, when they are lies, that listeners are better able to detect the lie than if the declarant has had time to prepare.
The rule, in other words, is based on assumptions that can be empirically tested. This Article surveys the scientific literature for insight into whether PSI statements have fewer memory errors and uses novel behavioral paradigms and electroencephalography (EEG) measures to test whether substantial contemporaneity is a safeguard against deceit. I conclude that PSI statements are likely to have fewer memory errors and the experimental results suggest that true contemporaneity, but not “substantial” contemporaneity, offers a degree of protection against deceit. Furthermore, the lies that do occur align with the protections inherent to the PSIR, with contemporaneous lies being more detectable by a third-party observer and lies about past events being susceptible to effective cross-examination.
Specifically, the results of the first experiment suggest that people switch cognitive strategies when lying about a contemporaneous event compared to a past event, employing a less working-memory-intensive strategy when given a delay. This switch lessens behavioral tells but may come at a cost to the overall memory of the event. The second experiment finds that even in a more behaviorally complex paradigm, individuals made significantly fewer errors and lied significantly more often when given a delay as short as three seconds to prepare their response, compared to a truly contemporaneous response prompted within 500 milliseconds.
Together, the results of the experiments suggest that the PSIR has a legitimate behavioral and cognitive basis: individuals switch between distinct cognitive strategies when lying about contemporaneous events compared to past events in a manner that supports the assumptions of the PSIR. The results also suggest, however, that this switch occurs almost immediately, and so the PSI exception should be restricted to only statements describing truly contemporaneous events
Lawyering and Ethics for the Business Attorney (6th edition)
No full textLegal counsel in their transactional and litigation practices for both privately- and publicly-traded enterprises must adhere to ethical norms and focus on their clients’ compliance with applicable law. With the continual presence of government as well as private litigation against business enterprises and their attorneys, preventative lawyering consistent with ethical standards is a key objective that merits priority in the law school curriculum. This interesting textbook, consisting of fourteen chapters covering a diverse range of subject matter, employs the problem method in combination with case law and issue analysis to make a meaningful contribution to the law school curriculum. Each chapter contains one or more Scenarios for students to analyze and solve in an ethical and law-compliant manner. These Scenarios focus on dilemmas addressing client identification, attorneys’ conflicts of interest in both current and successive representation, counseling the small business enterprise, client fraud, the business attorney as litigator, the attorney litigation privilege, insider trading compliance, attorneys entering into business transactions with their clients, the attorney acting as intermediary, the role of inside counsel, and legal malpractice avoidance. This user-friendly student text comfortably serves as the principal reading for the specialized business “lawyering” course as well as a focused practical and insightful supplemental text for the business associations and professional responsibility courses. The author, one of this country’s most prolific scholars, has extensive practical experience, including being a former SEC enforcement attorney and serving as an expert witness in several high-profile cases, including those involving Enron, Mark Cuban, and Martha Stewart. A thorough Professor’s Manual accompanies the text.https://scholar.smu.edu/facbooks/1075/thumbnail.jp
Advancing the Characterization of Geophysical Signals through Array Processing and Artificial Intelligence
No full textIn geophysics, seismic and infrasound observations are routinely employed to constrain the nature and origin of events. Seismoacoustics, as a discipline, is built upon the simultaneous detection and integrated analysis of these data types. This joint approach is critical not only for advancing scientific understanding but also for supporting global monitoring efforts in hazard mitigation and nuclear explosion treaty verification. The data analyzed in this dissertation were recorded by array deployments, which consist of multiple sensors arranged in predetermined configurations to enhance signal detection, resolve directionality, and quantify waveform coherence. Leveraging these array recordings, I introduce novel approaches which combine traditional signal processing techniques with advanced deep learning algorithms to augment signal detection rates, improve event discrimination, and enhance the characterization of infrasound phases. The dissertation begins with the development of Cardinal, an open-source multifrequency array processing software for seismic and infrasound data. Cardinal integrates a custom convolutional transformer model to predict optimal array configurations across sequential frequency bands, providing a more comprehensive framework for array analysis. The following chapter presents the development of a multimodal deep neural network that leverages adaptive gating to fuse seismic spectrograms with Cardinal infrasound array processing results. This fusion improves earthquake-explosion discrimination within the Korean Peninsula, surpassing unimodal approaches. The final chapter presents a novel framework for automatic infrasound phase identification, leveraging the latent representations of a pre-trained autoencoder to cluster distinct phases without requiring labeled data. Collectively, these contributions illustrate the potential of combining deep learning with seismoacoustic array data to advance geophysical research and improve the effectiveness of global monitoring networks
Econometric Analysis of Social Interactions and Decision-Making
No full textThis dissertation explores the econometric analysis of social interactions and decision-making. The first two chapters advance econometric methodology by addressing key challenges in identifying and estimating peer effects, specifically tackling issues related to partial network data and endogenous link formation. The third chapter presents an empirical analysis of parental over-aspiration and its impact on children\u27s academic outcomes, using monotone instrumental variables constructed from social network structures.
Chapter 1 is Identification and Estimation of Discrete Choice Models with Spillovers Using Partial Network Data . This paper investigates peer effects in discrete choice models with incomplete data on social links. We consider an undirected dyadic link formation model in which connections arise from homophily—similarities in individual characteristics—and unobserved individual fixed effects. Homophily effects are identified using the observed patterns of link formation among tetrads (groups of four individuals), while the distribution of individual fixed effects is identified through the configurations observed among triads (groups of three individuals). I propose an estimation strategy based on the estimated link formation probabilities to study peer effects on individual decision-making and establish its large sample properties. Simulations illustrate that the finite sample performance of the estimator is close to that obtained when the true network were known. Using data on household microfinance participation in rural India from Banerjee et al. (2013), where network data are available, I detect positive peer effects even with partial network data.
Chapter 2 is Inference for Social Interactions in Large Endogenous Networks (co-authored with Wan Zhang). We study the identification and estimation of social interactions in large endogenous networks. Our analysis focuses on binary-action games of incomplete information in which an agent\u27s expected payoff relies on her characteristics, peers\u27 average characteristics, the average of her beliefs about peers\u27 actions, and some preference shocks. Endogeneity in networks results from unobserved characteristics that affect both link formation and individual decision-making. To identify the utility functions, we express the unobserved characteristics as some unknown function of observed variables and address the endogeneity issue through a control function approach. The identification strategy holds even if multiple equilibria exist. We employ the strategy to develop a semiparametric estimator and assess finite-sample performance through Monte Carlo simulations.
Chapter 3 is Echoes of Icarus: How Parental Over-Aspiration Shapes Children\u27s Academic Success . This paper documents the negative influence of overly ambitious parental aspirations on children\u27s academic outcomes, utilizing data from the Education Longitudinal Study 2002. After controlling for family socioeconomic status, I find that parents with better education are less likely to form overly ambitious aspirations, which represents a new channel to account for the positive effects of parental education on children\u27s academic performances. To address the possibility that parental aspirations are endogenous, I construct instruments based on children\u27s social network. For robustness, I use an alternative approach based on bounding the effect of parental aspirations under weaker assumptions. The results are consistent across both approaches
Systemic Risk and the Social Contract
Get PDFThe stability of the constitutional order turns, in part, on a stable economy and reliable advances in technology. Political order cannot withstand economic collapse or a massive technological failure. Such crises chip away at the collective will to adhere to the social contract because they indicate the government may not have the capacity to uphold its end of the bargain—protecting individual liberty from broad threats. “Unprecedented” economic downturns, however, have a precedent of emerging from the very deliberate decision of some actors to pursue extremely risky behavior in their self-interest at the expense of the public. Societal disruption from over-dependence on specific technology is also the product of known factors. Governments have no excuse, then, for serially allowing the risky behavior of a few to imperil the political order upon which the many rely for liberty, opportunity, and stability. This is not to say that detecting and stemming such risky behavior before a crisis occurs is easy. It is not. Ignorance, though, can no longer serve as an excuse for governments not taking more seriously systemic risks to the economy and, by extension, the political order. The transition from the Articles of Confederation to the Constitution and the text of early state constitutions make clear that both levels of government must proactively and successfully mitigate systemic risks. Overreliance on AI could cause severe economic and technological chaos upon some failure. If governments allow such risks to go unaddressed, they will be in violation of the social contract—a fact that mandates that state and federal officials do more than simply try to adjust old regulatory frameworks, such as antitrust law, to this novel risk. A few policy solutions could demonstrate a good-faith effort to shield people if AI does indeed go south. None of the solutions will alone mitigate the risks of excessive reliance on a few AI labs. First, the state and federal governments can insist on a diversified AI portfolio in their own procurement practices. The government’s purchasing power can induce competition in the AI space-chipping away at the dominance of the first scalers, such as OpenAI, in the field. Knowledge of lucrative contracts with the federal government could be the seed of several startups that grow to become key players in the market. Second, state legislatures and Congress should explore imposing an insurance requirement on the largest AI labs. If you build it and break it, then you should pay for it. This approach has historical and modern precedent. As far back as World War I, the government took active steps to ensure against worst-case economic scenarios during turbulent times. Other strategies abound and should be proposed and explored. The key is that the government should not sit on the sidelines and allow further risks to develop
Disenchanting Consent
Get PDFDespite being criticized as a flawed mechanism for data protection, con- sent has witnessed a revival in the recent wave of state privacy statutes. One factor that contributed to the revival may be the widely held belief that con- sent constitutes the “cornerstone” of data privacy laws. This Article conducts a comparative historical survey to examine the validity of this belief. The findings are twofold. First, contrary to what many believe, consent has been playing a limited role in global data privacy laws. Second, consent is an inherently defective mechanism for data protection. Some of its problems have existed since the inception of modern data privacy laws, and attempts to address them and safeguard consent will likely be doomed. These findings disenchant con- sent and signify the need to look beyond it. Reform efforts should focus on other areas of data privacy laws, a lesson with implications for states with new laws or bills that reinforce the role of consent
My Time with the Fusion Industry Association (“FIA”): Future-Focused Policy for a Fusion-Powered America
Get PDFSecurities Practice: Federal and State Enforcement (2025-2026 edition)
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Gain insight into the workings of the Securities Exchange Commission (SEC). This reference describes the SEC’s approach to enforcement to help you respond effectively to an SEC investigation or enforcement action. Features Regulation of futures trading SEC subpoena enforcement practice Administrative remedies and injunctions Applicability of non-securities laws to SEC enforcement practice Covers multiple representation and conflicts of interest; attorney-client privilege Discusses SEC investigations Criminal and related enforcement practic
LLMs Are Bad Judges. So Use a Classifier Instead.
No full textLarge Language Models suffer from prompt variance—meaning they’ll give you totally different legal answers depending on how you phrase your question. Jonathan Choi demonstrated this recently when he asked ChatGPT five legal questions, each rephrased 2,000 times, and watched as the bot spat out different answers every time. When you tell somebody that AI is going to replace the judge, the lawyer, and the legal system in the next twenty years, Choi’s article has become the go-to rebuttal; it’s the crown jewel of the “AI bad” genre.
Choi’s absolutely right that LLM’s are bad judges. And, if every AI was an LLM, your BigLaw job would be safe. But there’s this other type of AI — it’s a classifier, and we built one called Arbitrus. We put it through a mini-Choi test and it mopped the floor with the competition, delivering perfect consistency across all prompt variants with zero hallucinations. We’re going to tell you how it works, why it’s better than an LLM and, ultimately, why it’s better than you. And we’ll close with the frank assertion that a judge’s highest telos—in any legal system—is amendable consistency. That means the Choi test, simple though it may seem, isn’t just a “gotcha” for chatbots; it’s the defining test of judicial qualification