Making Decisions under Outcome Performativity

Decision-makers often act in response to data-driven predictions, with the goal of achieving favorable outcomes. In such settings, predictions don't passively forecast the future; instead, predictions actively shape the distribution of outcomes they are meant to predict. This performative prediction setting raises new challenges for learning "optimal" decision rules. In particular, existing solution concepts do not address the apparent tension between the goals of forecasting outcomes accurately and steering individuals to achieve desirable outcomes. To contend with this concern, we introduce a new optimality concept -- performative omniprediction -- adapted from the supervised (non-performative) learning setting. A performative omnipredictor is a single predictor that simultaneously encodes the optimal decision rule with respect to many possibly-competing objectives. Our main result demonstrates that efficient performative omnipredictors exist, under a natural restriction of performative prediction, which we call outcome performativity. On a technical level, our results follow by carefully generalizing the notion of outcome indistinguishability to the outcome performative setting. From an appropriate notion of Performative OI, we recover many consequences known to hold in the supervised setting, such as omniprediction and universal adaptability

Procesos de significación y construcción de sentido que sobre las tecnologías elaboran profesores de educación primaria

ITESO, A. C

Difficult Lessons on Social Prediction from Wisconsin Public Schools

Early warning systems (EWS) are prediction algorithms that have recently taken a central role in efforts to improve graduation rates in public schools across the US. These systems assist in targeting interventions at individual students by predicting which students are at risk of dropping out. Despite significant investments and adoption, there remain significant gaps in our understanding of the efficacy of EWS. In this work, we draw on nearly a decade's worth of data from a system used throughout Wisconsin to provide the first large-scale evaluation of the long-term impact of EWS on graduation outcomes. We present evidence that risk assessments made by the prediction system are highly accurate, including for students from marginalized backgrounds. Despite the system's accuracy and widespread use, we find no evidence that it has led to improved graduation rates. We surface a robust statistical pattern that can explain why these seemingly contradictory insights hold. Namely, environmental features, measured at the level of schools, contain significant signal about dropout risk. Within each school, however, academic outcomes are essentially independent of individual student performance. This empirical observation indicates that assigning all students within the same school the same probability of graduation is a nearly optimal prediction. Our work provides an empirical backbone for the robust, qualitative understanding among education researchers and policy-makers that dropout is structurally determined. The primary barrier to improving outcomes lies not in identifying students at risk of dropping out within specific schools, but rather in overcoming structural differences across different school districts. Our findings indicate that we should carefully evaluate the decision to fund early warning systems without also devoting resources to interventions tackling structural barriers

In vitro hypercoagulability and ongoing in vivo activation of coagulation and fibrinolysis in COVID-19 patients on anticoagulation

Background COVID-19 is associated with a substantial risk of venous thrombotic events, even in the presence of adequate thromboprophylactic therapy. Objectives We aimed to better characterize the hypercoagulable state of COVID-19 patients in patients receiving anticoagulant therapy. Methods We took plasma samples of 23 patients with COVID-19 who were on prophylactic or intensified anticoagulant therapy. Twenty healthy volunteers were included to establish reference ranges. Results COVID-19 patients had a mildly prolonged prothrombin time, high von Willebrand factor levels and low ADAMTS13 activity. Most rotational thromboelastometry parameters were normal, with a hypercoagulable maximum clot firmness in part of the patients. Despite detectable anti-activated factor X activity in the majority of patients, ex vivo thrombin generation was normal, and in vivo thrombin generation elevated as evidenced by elevated levels of thrombin-antithrombin complexes and D-dimers. Plasma levels of activated factor VII were lower in patients, and levels of the platelet activation marker soluble CD40 ligand were similar in patients and controls. Plasmin-antiplasmin complex levels were also increased in patients despite an in vitro hypofibrinolytic profile. Conclusions COVID-19 patients are characterized by normal in vitro thrombin generation and enhanced clot formation and decreased fibrinolytic potential despite the presence of heparin in the sample. Anticoagulated COVID-19 patients have persistent in vivo activation of coagulation and fibrinolysis, but no evidence of excessive platelet activation. Ongoing activation of coagulation despite normal to intensified anticoagulant therapy indicates studies on alternative antithrombotic strategies are urgently required

A Comparison of Lymphoid and Myeloid Cells Derived from Human Hematopoietic Stem Cells Xenografted into NOD-Derived Mouse Strains

Humanized mice are an invaluable tool for investigating human diseases such as cancer, infectious diseases, and graft-versus-host disease (GvHD). However, it is crucial to understand the strengths and limitations of humanized mice and select the most appropriate model. In this study, we describe the development of the human lymphoid and myeloid lineages using a flow cytometric analysis in four humanized mouse models derived from NOD mice xenotransplanted with CD34+ fetal cord blood from a single donor. Our results showed that all murine strains sustained human immune cells within a proinflammatory environment induced by GvHD. However, the Hu-SGM3 model consistently generated higher numbers of human T cells, monocytes, dendritic cells, mast cells, and megakaryocytes, and a low number of circulating platelets showing an activated profile when compared with the other murine strains. The hu-NOG-EXL model had a similar cell development profile but a higher number of circulating platelets with an inactivated state, and the hu-NSG and hu-NCG developed low frequencies of immune cells compared with the other models. Interestingly, only the hu-SGM3 and hu-EXL models developed mast cells. In conclusion, our findings highlight the importance of selecting the appropriate humanized mouse model for specific research questions, considering the strengths and limitations of each model and the immune cell populations of interest. © 2023 by the authors