Naïve Learning with Uninformed Agents

2021 ◽  
Vol 111 (11) ◽  
pp. 3540-3574
Author(s):  
Abhijit Banerjee ◽  
Emily Breza ◽  
Arun G. Chandrasekhar ◽  
Markus Mobius

The DeGroot model has emerged as a credible alternative to the standard Bayesian model for studying learning on networks, offering a natural way to model naïve learning in a complex setting. One unattractive aspect of this model is the assumption that the process starts with every node in the network having a signal. We study a natural extension of the DeGroot model that can deal with sparse initial signals. We show that an agent’s social influence in this generalized DeGroot model is essentially proportional to the degree-weighted share of uninformed nodes who will hear about an event for the first time via this agent. This characterization result then allows us to relate network geometry to information aggregation. We show information aggregation preserves “wisdom” in the sense that initial signals are weighed approximately equally in a model of network formation that captures the sparsity, clustering, and small-world properties of real-world networks. We also identify an example of a network structure where essentially only the signal of a single agent is aggregated, which helps us pinpoint a condition on the network structure necessary for almost full aggregation. Simulating the modeled learning process on a set of real-world networks, we find that there is on average 22.4 percent information loss in these networks. We also explore how correlation in the location of seeds can exacerbate aggregation failure. Simulations with real-world network data show that with clustered seeding, information loss climbs to 34.4 percent. (JEL D83, D85, Z13)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marios Papachristou

AbstractIn this paper we devise a generative random network model with core–periphery properties whose core nodes act as sublinear dominators, that is, if the network has n nodes, the core has size o(n) and dominates the entire network. We show that instances generated by this model exhibit power law degree distributions, and incorporates small-world phenomena. We also fit our model in a variety of real-world networks.


2021 ◽  
pp. 009365022110161
Author(s):  
Adam J. Saffer ◽  
Andrew Pilny ◽  
Erich J. Sommerfeldt

Recent interorganizational communication research has taken up the question: why are networks structured the way they are? This line of inquiry has advanced communication network research by helping explain how and why networks take on certain structures or why certain organizations become positioned advantageously (or not). Yet, those studies assume relationships among organizations are either present or absent. This study considers how the strength of ties and multiplex relationships among organizations may reveal a more complex explanation for why networks take on certain structures. Our results challenge some long held assumptions about the mechanisms that influence network formation. For instance, our results offer important insights into the consequences of closure mechanisms, the applicability of preferential attachment to real-world networks, and the nuances of homophily in network formation on multidimensional relationships in a communication network. Implications for interorganizational networks are discussed.


2017 ◽  
Vol 117 (9) ◽  
pp. 1866-1889 ◽  
Author(s):  
Vahid Shokri Kahi ◽  
Saeed Yousefi ◽  
Hadi Shabanpour ◽  
Reza Farzipoor Saen

Purpose The purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score. Design/methodology/approach A dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach. Findings This paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately. Research limitations/implications In real world, managers face with big data. Therefore, we need to develop an approach to deal with big data. Practical implications The proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc. Originality/value For the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.


Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2440
Author(s):  
Francesco Spagnolo ◽  
Bruna Dalmasso ◽  
Enrica Tanda ◽  
Miriam Potrony ◽  
Susana Puig ◽  
...  

Inherited pathogenic variants (PVs) in the CDKN2A tumor suppressor gene are among the strongest risk factors for cutaneous melanoma. Dysregulation of the p16/RB1 pathway may intrinsically limit the activity of MAPK-directed therapy due to the interplay between the two pathways. In our study, we assessed, for the first time, whether patients with germline CDKN2A PVs achieve suboptimal results with BRAF inhibitors (BRAFi)+/−MEK inhibitors (MEKi). We compared the response rate of nineteen CDKN2A PVs carriers who received first-line treatment with BRAFi+/−MEKi with an expected rate derived from phase III trials and “real-world” studies. We observed partial response in 16/19 patients (84%), and no complete responses. The overall response rate was higher than that expected from phase III trials (66%), although not statistically significant (p-value = 0.143; 95% CI = 0.60–0.97); the difference was statistically significant (p-value = 0.019; 95% CI = 0.62–0.97) in the comparison with real-world studies (57%). The clinical activity of BRAFi+/−MEKi in patients with germline CDKN2A PV was not inferior to that of clinical trials and real-world studies, which is of primary importance for clinical management and genetic counseling of this subgroup of patients.


2020 ◽  
Vol 31 ◽  
pp. S1439
Author(s):  
G. Sharat Chandra ◽  
M. Singhal ◽  
A. Sharma ◽  
S. Valame ◽  
D. Panda ◽  
...  

2020 ◽  
Vol 30 (02) ◽  
pp. 2050009
Author(s):  
Qifan Zhang ◽  
Liqiong Xu ◽  
Weihua Yang ◽  
Shanshan Yin

Let [Formula: see text] be a non-complete graph, a subset [Formula: see text] is called a [Formula: see text]-component cut of [Formula: see text], if [Formula: see text] is disconnected and has at least [Formula: see text] components. The cardinality of the minimum [Formula: see text]-component cut is the [Formula: see text]-component connectivity of [Formula: see text] and is denoted by [Formula: see text]. The [Formula: see text]-component connectivity is a natural extension of the classical connectivity. As an application, the [Formula: see text]-component connectivity can be used to evaluate the reliability and fault tolerance of an interconnection network structure based on a graph model. In a previous work, E. Cheng et al. obtained the [Formula: see text]-component connectivity of the generalized exchanged hypercube [Formula: see text] for [Formula: see text] and [Formula: see text]. In this paper, we continue the work and determine that [Formula: see text] for [Formula: see text]. Moreover, we show that every optimal [Formula: see text]-component cut of [Formula: see text] is trivial for [Formula: see text] and [Formula: see text].


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 59833-59842 ◽  
Author(s):  
Wenchao Jiang ◽  
Yinhu Zhai ◽  
Zhigang Zhuang ◽  
Paul Martin ◽  
Zhiming Zhao ◽  
...  

2009 ◽  
Vol 24 (40) ◽  
pp. 3275-3282 ◽  
Author(s):  
LIJING SHAO ◽  
BO-QIANG MA

A phenomenological law, called Benford's law, states that the occurrence of the first digit, i.e. 1, 2,…, 9, of numbers from many real world sources is not uniformly distributed, but instead favors smaller ones according to a logarithmic distribution. We investigate, for the first time, the first digit distribution of the full widths of mesons and baryons in the well-defined science domain of particle physics systematically, and find that they agree excellently with the Benford distribution. We also discuss several general properties of Benford's law, i.e. the law is scale-invariant, base-invariant and power-invariant. This means that the lifetimes of hadrons also follow Benford's law.


2014 ◽  
Vol 13 (2) ◽  
Author(s):  
Jaromír Kovářík ◽  
Marco J. van der Leij

AbstractThis paper first investigates empirically the relationship between risk aversion and social network structure in a large group of undergraduate students. We find that risk aversion is strongly correlated to local network clustering, that is, the probability that one has a social tie to friends of friends. We then propose a network formation model that generates this empirical finding, suggesting that locally superior information on benefits makes it more attractive for risk averse individuals to link to friends of friends. Finally, we discuss implications of this model. The model generates a positive correlation between local network clustering and benefits, even if benefits are distributed independently ex ante. This provides an alternative explanation of this relationship to the one given by the social capital literature. We also establish a linkage between the uncertainty of the environment and the network structure: risky environments generate more clustered and more unequal networks in terms of connectivity.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19512-e19512
Author(s):  
Kyeryoung Lee ◽  
Zongzhi Liu ◽  
Meng Ma ◽  
Yun Mai ◽  
Christopher Gilman ◽  
...  

e19512 Background: Targeted therapy is an important treatment for chronic lymphocytic leukemia (CLL). However, optimal strategies for deploying small molecule inhibitors or antibody therapies in the real world are not well understood, largely due to a lack of outcomes data. We implemented a novel temporal phenotyping algorithm pipeline to derive lines of therapy (LOT) and disease progression in CLL patients. Here, the CLL treatment pattern and time to the next treatment (TTNT) were analyzed in real-world data (RWD) using patient electronic health records. Methods: We identified a CLL cohort with LOT from the Mount Sinai Data Warehouse (2003-2020). Each LOT consisted of either a single agent or combinations defined by NCCN CLL guidelines. We developed a natural language processing (NLP)-based temporal phenotyping approach to automatically identify the number of lines and therapeutic regimens. The sequence of treatment and time interval for each patient were derived from the systematic treatment data. Time to event analysis and multivariate (i.e., age, gender, race, other treatment patterns) Cox proportional hazard (CoxPH) models were used to analyze the patterns and predictors of TTNT. Results: Four hundred eleven CLL patients received 1 to 7 LOTs. Ibrutinib was the predominant 1st LOT (40.8% of patients) followed by anti-CD20-based antibody therapies and chemotherapy in 30.6 and 19.2% of patients, respectively, followed by Acalabrutinib, Venetoclax, and Idelalisib in 3.4, 2.7, and 0.7% of patients, respectively (Table 1). The 2nd to 5th LOT showed the same or similar trends. We next analyzed the TTNT in the 1st line of each therapeutic class. Acalabrutinib resulted in a longer median TTNT than Ibrutinib. Both Acalabrutinib and Ibrutinib showed longer TTNT compared to Venetoclax (median TTNTs were 742 and 598 vs. 373 days: HR = 0.23, p=0.015 and HR = 0.48, p=0.03, respectively). In addition, patients with age equal to or older than 65 showed longer TNNT (HR=0.16, p=0.016). Conclusions: Our result shows the potential of RWD usage in clinical decision making as real-world evidence reported here is consistent with results derived from clinical trial data. Linking this study to genetic data and other covariates affecting treatment outcomes may provide additional insights into the optimal sequences of the targeted therapies in CLL. Table 1: Therapeutic class and patient numbers (%) in each line.[Table: see text]


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