Voluntary contributions in cascades: The tragedy of ill-informed leadership

URL des Documents de travail : https://centredeconomiesorbonne.univ-paris1.fr/documents-de-travail-du-ces/Documents de travail du Centre d'Economie de la Sorbonne 2020.23 - ISSN : 1955-611XVoluntary contributions are often solicited in sequential and public settings where information on the quality of the fundraising project unfolds with the sequence of decisions. This paper examines how the different sources of information available to potential donors in such settings influence their decision-making. Contrary to most of the leadership literature, neither leaders nor followers in these settings have certainty about the quality of the fundraising project. We explore whether leaders remain influential, the extent to which they use their influence strategically, and the consequences on followers when leaders are misinformed. We combine an information cascade method with a modified public goods game to create a “Voluntary Contributions in Cascades” paradigm. Participants sequentially receive private signals about the state of the world, which determines the potential returns from the public good, and take two public actions: an incentivized prediction about the state of the world and a contribution to the public good. We find that participants' predictions mostly align with Bayesian predictions, and find no evidence for strategic or misleading predictions. Leaders' contributions are positively correlated with followers', suggesting they remain influential despite their limited informational advantage. This influence takes a tragic turn when leaders happen to be misinformed, as most misinformed leaders end up unintentionally misleading followers. We find that having a misleading leader is associated with a reduction in gains from contributions roughly twice as large as the reduction that stems from dividing the marginal-per-capita-return by two. Our results stress the significance of having well-informed leaders

Individual differences in cerebral metabolic patterns during pharmacotherapy in obsessive-compulsive disorder: A multiple regression/discriminant analysis of positron emission tomographic data

A multiple regression/discriminant analysis of positron emission tomographic cerebral metabolic (rCMRglc) data in 10 obsessive-compulsive disorder (OCD) patients before and during pharmacotherapy was carried out to see if rCMRglc interdependencies distinguished OCD patients from controls. Before therapy, a discriminant function reflecting parietal, sensorimotor, and midbrain rCMRglc interdependencies correctly classified eight (80) of the 10 patients as OCD; after therapy, six (70) were classified as controls, most of whom were responders. Before therapy, rCMRglc interdependencies involving basal ganglia, thalamus, limbic, and sensory and association cortical regions distinguished 67 of patients who clinically responded to drug (RESP, n = 6) and 75 of patients who did not (NRESP, n = 4) from controls. After therapy, all RESP were classified as controls; classification of NRESP remained unchanged. The results suggest the conjunctive utility of this method to assess individual differences in rCMRglc during pharmacotherapy, and to explore the neurobiology of OCD

Early Detection of Alzheimer's Disease: A Statistical Approach Using Positron Emission Tomographic Data

Correlational analysis of regional cerebral glucose metabolism (rCMRglc) obtained by high-resolution positron emission tomography (PET) has demonstrated reduced neocortical rCMRglc interactions in mildly/moderately demented patients with probable Alzheimer's disease (AD). Thus, identification of individual differences in patterns of rCMRglc interactions may be important for the early detection of AD, particularly among individuals at greater risk for developing AD (e.g., those with a family history of AD). Recently, a statistical procedure, using multiple regression and discriminant analysis, was developed to assess individual differences in patterns of rCMRglc interdependencies. We applied this new statistical procedure to resting rCMRglc PET data from mildly/moderately demented patients with probable AD and age/sex-matched controls. The aims of the study were to identify a discriminant function that would (a) distinguish patients from controls and (b) identify an AD pattern in an individual at risk for AD with isolated memory impairment whose initial PET scan showed minor abnormalities, but whose second scan showed parietal hypometabolism, coincident with further cognitive decline. Two discriminant functions, reflecting interactions involving regions most involved in reduced correlations in probable AD, correctly classified 87 of the patients and controls, and successfully identified the first scan of the at-risk individual as AD (probability >0.70). The results suggest that this statistical approach may be useful for the early detection of AD

Quantitative analysis of magnetic resonance images for characterization of blood‐brain barrier dysfunction in dogs with brain tumors

Abstract Background Blood‐brain barrier (BBB) permeability can be assessed quantitatively using advanced imaging analysis. Hypothesis/Objectives Quantification and characterization of blood‐brain barrier dysfunction (BBBD) patterns in dogs with brain tumors can provide useful information about tumor biology and assist in distinguishing between gliomas and meningiomas. Animals Seventy‐eight hospitalized dogs with brain tumors and 12 control dogs without brain tumors. Methods In a 2‐arm study, images from a prospective dynamic contrast‐enhanced (DCE; n = 15) and a retrospective archived magnetic resonance imaging study (n = 63) were analyzed by DCE and subtraction enhancement analysis (SEA) to quantify BBB permeability in affected dogs relative to control dogs (n = 6 in each arm). For the SEA method, 2 ranges of postcontrast intensity differences, that is, high (HR) and low (LR), were evaluated as possible representations of 2 classes of BBB leakage. BBB score was calculated for each dog and was associated with clinical characteristics and tumor location and class. Permeability maps were generated, using the slope values (DCE) or intensity difference (SEA) of each voxel, and analyzed. Results Distinctive patterns and distributions of BBBD were identified for intra‐ and extra‐axial tumors. At a cutoff of 0.1, LR/HR BBB score ratio yielded a sensitivity of 80% and specificity of 100% in differentiating gliomas from meningiomas. Conclusions and Clinical Importance Blood‐brain barrier dysfunction quantification using advanced imaging analyses has the potential to be used for assessment of brain tumor characteristics and behavior and, particularly, to help differentiating gliomas from meningiomas

The Society for Immunotherapy of Cancer consensus statement on immunotherapy for the treatment of multiple myeloma.

Outcomes in multiple myeloma (MM) have improved dramatically in the last two decades with the advent of novel therapies including immunomodulatory agents (IMiDs), proteasome inhibitors and monoclonal antibodies. In recent years, immunotherapy for the treatment of MM has advanced rapidly, with the approval of new targeted agents and monoclonal antibodies directed against myeloma cell-surface antigens, as well as maturing data from late stage trials of chimeric antigen receptor CAR T cells. Therapies that engage the immune system to treat myeloma offer significant clinical benefits with durable responses and manageable toxicity profiles, however, the appropriate use of these immunotherapy agents can present unique challenges for practicing physicians. Therefore, the Society for Immunotherapy of Cancer convened an expert panel, which met to consider the current role of approved and emerging immunotherapy agents in MM and provide guidance to the oncology community by developing consensus recommendations. As immunotherapy evolves as a therapeutic option for the treatment of MM, these guidelines will be updated

The Society for Immunotherapy of Cancer consensus statement on immunotherapy for the treatment of multiple myeloma

Outcomes in multiple myeloma (MM) have improved dramatically in the last two decades with the advent of novel therapies including immunomodulatory agents (IMiDs), proteasome inhibitors and monoclonal antibodies. In recent years, immunotherapy for the treatment of MM has advanced rapidly, with the approval of new targeted agents and monoclonal antibodies directed against myeloma cell-surface antigens, as well as maturing data from late stage trials of chimeric antigen receptor (CAR) T cells. Therapies that engage the immune system to treat myeloma offer significant clinical benefits with durable responses and manageable toxicity profiles, however, the appropriate use of these immunotherapy agents can present unique challenges for practicing physicians. Therefore, the Society for Immunotherapy of Cancer convened an expert panel, which met to consider the current role of approved and emerging immunotherapy agents in MM and provide guidance to the oncology community by developing consensus recommendations. As immunotherapy evolves as a therapeutic option for the treatment of MM, these guidelines will be updated