Do Consumers Pay for One-Stop Banking?

The authors use a specialized revenue function to estimate the revenue economies of scope and determine whether banks providing a broad mix of services are able to capitalize on the potential savings in transaction costs afforded their customers. Bank production costs and consumer consumption expenses are thought to be reduced through relationship banking strategies that cultivate one stop shopping for financial services. Much work has been done on estimates of production synergies that correspond to cost economies of scope. The complementarities in the consumption of bank services is potentially as important for bank profitability but it has yet to be examined in detail. Complementarities arise from reductions in user transaction and search costs associated with consuming financial services jointly from the same bank provider, often at the same location, rather than consuming these services separately from different providers at different locations. If benefits from joint consumption are strong, consumers should be willing to pay for them through higher prices at banks that provide services jointly rather than separately. The authors find no evidence of statistically significant revenue complementarities or fixed revenue effects among banks over 1978-1990. Revenues are no larger when deposits and loans are provided jointly rather than separately, and consumers do not pay for one stop banking. This holds for the average small or large bank as well as those on and off the revenue-efficient frontier. Combining revenue scope results with earlier cost scope findings suggests that synergies between bank deposits and loans are small and concentrated in joint production, rather than joint consumption. Consumers may or may not value one stop banking, but they apparently do not have to pay for it.

Approaches to suggest potential agreements: Perspectives of mediation with incomplete information

In bilateral Negotiation Analysis, the literature often co nsiders the case of complete information. In this context, since the negotiators know the value functions of both parties, it is not difficult to calculate the Pareto frontier and the Pareto efficient soluti ons for the negotiation. Thus rational negotiators can reach agreement on this frontier. However, these approaches are not applied in practice when the parties do not have complete information. The research question of our work is “It is possible to help negotiators achieving an efficient soluti on if they do not have complete information regarding the different parameters of the model?”. We propos e to obtain information regarding the preferences of negotiators during the negotiation process , in order to be able to propose alternatives close to the Pareto frontier. During this work we will presen t three approaches to help a mediator proposing a better solution than the compromise the negotia tors have reached or are close to reach

From task structures to world models: What do LLMs know?

In what sense does a large language model have knowledge? The answer to this question extends beyond the capabilities of a particular AI system, and challenges our assumptions about the nature of knowledge and intelligence. We answer by granting LLMs "instrumental knowledge"; knowledge defined by a certain set of abilities. We then ask how such knowledge is related to the more ordinary, "worldly" knowledge exhibited by human agents, and explore this in terms of the degree to which instrumental knowledge can be said to incorporate the structured world models of cognitive science. We discuss ways LLMs could recover degrees of worldly knowledge, and suggest such recovery will be governed by an implicit, resource-rational tradeoff between world models and task demands

Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces

Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known as grammatical inference. We also introduce a measure of similarity that accounts for the intrinsic hierarchical nature of such models, and allows to identify the common traits in the paths induced by different activities at various granularity levels. Our method has been validated on a dataset of real traces representing movements of users in a large metropolitan area. The experimental results show the effectiveness of our similarity measure to correctly identify a set of common coarse-grained activities, as well as their refinement at a finer level of granularity

Machine learning in scientific grant review: algorithmically predicting project efficiency in high energy physics

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model

Gender mainstreaming research funding: a study of effects on STEM research proposals

Policymakers increasingly try to steer researchers to choose topics of societal concern and to conduct research in ways that reflect such concerns. One increasingly common approach is prompting researchers to integrate certain perspectives into the content of their research, but little is known about the effects of this governance modality. We analyze 1,189 science, technology, engineering, and mathematics research proposals submitted to the Swedish Research Council which, starting in 2020, required all applicants to consider including the sex and/or gender perspectives in their research. We identify three overarching strategies upon which researchers rely (content-, performer-, and impact-centered) and analyze the ways in which researchers across disciplines motivate, through text, the inclusion or exclusion of these perspectives. Based on our findings, we discuss the scope of the desired effect(s) of a requirement of this kind

Sensorimotor coarticulation in the execution and recognition of intentional actions

Humans excel at recognizing (or inferring) another's distal intentions, and recent experiments suggest that this may be possible using only subtle kinematic cues elicited during early phases of movement. Still, the cognitive and computational mechanisms underlying the recognition of intentional (sequential) actions are incompletely known and it is unclear whether kinematic cues alone are sufficient for this task, or if it instead requires additional mechanisms (e.g., prior information) that may be more difficult to fully characterize in empirical studies. Here we present a computationally-guided analysis of the execution and recognition of intentional actions that is rooted in theories of motor control and the coarticulation of sequential actions. In our simulations, when a performer agent coarticulates two successive actions in an action sequence (e.g., "reach-to-grasp" a bottle and "grasp-to-pour"), he automatically produces kinematic cues that an observer agent can reliably use to recognize the performer's intention early on, during the execution of the first part of the sequence. This analysis lends computational-level support for the idea that kinematic cues may be sufficiently informative for early intention recognition. Furthermore, it suggests that the social benefits of coarticulation may be a byproduct of a fundamental imperative to optimize sequential actions. Finally, we discuss possible ways a performer agent may combine automatic (coarticulation) and strategic (signaling) ways to facilitate, or hinder, an observer's action recognition processe

Toward predicting research proposal success

© 2017, Akadémiai Kiadó, Budapest, Hungary. Citation analysis and discourse analysis of 369 R01 NIH proposals are used to discover possible predictors of proposal success. We focused on two issues: the Matthew effect in science—Merton’s claim that eminent scientists have an inherent advantage in the competition for funds—and quality of writing or clarity. Our results suggest that a clearly articulated proposal is more likely to be funded than a proposal with lower quality of discourse. We also find that proposal success is correlated with a high level of topical overlap between the proposal references and the applicant’s prior publications. Implications associated with the analysis of proposal data are discussed.https://deepblue.lib.umich.edu/bitstream/2027.42/150071/2/Predicting_Proposal_Success_rev0_hdr.pdfPublished versionDescription of Predicting_Proposal_Success_rev0_hdr.pdf : Accepted versio