Strategic Auditing: An Incomplete Information Model

2001 ◽  
Vol 28 (5-6) ◽  
pp. 631-652 ◽  
Author(s):  
Peter Cheng ◽  
Bradley D. Childs ◽  
William W. Sheng
Keyword(s):  
Incomplete Information ◽  
Information Model

scholarly journals Empirical Pricing of Chinese Defaultable Corporate Bonds Based on the Incomplete Information Model

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Li Ping ◽  
Wang Xiaoxu
Keyword(s):  
Incomplete Information ◽  
Default Risk ◽  
Corporate Bonds ◽  
Information Model ◽  
Corporate Bond ◽  
Reduced Form ◽  
First Year ◽  
The Real ◽  
Form Model

The default of Suntech Power made the year 2013 in China “the first year of default” of bond markets. People are also clearly aware of the default risk of corporate bonds and find that fair pricing for defaultable corporate bonds is very important. In this paper we first give the pricing model based on incomplete information, then empirically price the Chinese corporate bond “11 super JGBS” from Merton’s model, reduced-form model, and incomplete information model, respectively, and then compare the obtained prices with the real prices. Results show that all the three models can reflect the trend of bond prices, but the incomplete information model fits the real prices best. In addition, the default probability obtained from the incomplete information model can discriminate the credit quality of listed companies.

Deterrence in the Cyber Realm: Public versus Private Cyber Capacity

2021 ◽  
Author(s):  
Nadiya Kostyuk
Keyword(s):  
Incomplete Information ◽  
Information Model ◽  
Cyber Attacks ◽  
Policy Implications ◽  
Weak States ◽  
Important Policy ◽  
Significant Damage ◽  
Middle Powers ◽  
Public Versus Private ◽  
Cyber Deterrence

Abstract Can cyber deterrence work? Existing scholarly works argue that deterrence by punishment using cyberattacks is ineffective because the difficulty of attributing the origin of cyberattacks makes the threat of future attacks less credible. However, these works have told us relatively little about the deterrence ability of public cyberinstitutions (PCIs), defined as publicly observable proactive efforts aimed at signaling a country’s level of cyber offensive and defensive capability. This research shows that middle powers (that have scarce cyber arsenals) can use PCIs to deter cyber attacks that cause significant damage to their economy and prosperity; however, this deterrent capability is rather limited. Using an incomplete-information model, we demonstrate that PCIs only deter adversaries that are susceptible to the costs created by these institutions. Despite this limited deterrence ability, middle powers tend to over-invest resources in these cyberinstitutions: Weak cyber states tend to over-invest to convince strong cyber adversaries that they are strong, whereas strong cyber states over-invest so that adversaries do not believe that they are weak states pretending to be strong. By doing so, states reduce their overall cybercapacity. We establish the empirical plausibility of these results using election interference campaigns as examples of strategic attacks. Our focus on the strategic use of PCIs as a deterrent represents a departure from existing literature—which has focused only on cyberoperations—and has important policy implications.

Game Theory and the Spiral Model

1997 ◽  
Vol 49 (3) ◽  
pp. 371-400 ◽  
Author(s):  
Andrew Kydd
Keyword(s):  
Game Theory ◽  
Incomplete Information ◽  
Information Model ◽  
Arms Race ◽  
Weak States ◽  
Arms Races ◽  
Military Buildup ◽  
Spiral Model ◽  
Psychological Bias

When one state engages in a military buildup, other states sometimes take this as a sign that it is more aggressive or expansionist than they previously thought. Some argue that such increases in mutual suspicion can drive arms races and even lead to war. Psychological bias is often invoked to explain this pattern of growing suspicions leading to hostility. This article presents an incomplete information model of an arms race and investigates when escalations should rationally generate increased fears and when, in order to reduce such fears, security seekers can refrain from building. It shows that escalations rationally provoke fear even in the absence of bias and that weak states and states facing high costs of arms racing and war will be especially likely to refrain from building as a way of signaling benign intentions.

scholarly journals Analysis of Signal Game for Supply Chain Finance (SCF) of MSEs and Banks Based on Incomplete Information Model

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Zhang Tao ◽  
Xin Li ◽  
Xinquan Liu ◽  
Nana Feng
Keyword(s):  
Supply Chain ◽  
Incomplete Information ◽  
Commercial Banks ◽  
Information Model ◽  
Business Environment ◽  
Small Enterprises ◽  
Model Based ◽  
Supply Chain Finance

The signal gaming model based on incomplete information is used to analyze the decisions of commercial banks and medium-sized and small enterprises (SMEs) in supply chain finance business. It is found that the returns of banks are closely relied on the probability of good SMEs join which is proportional to θ (the probability of “good” SMEs in the market) and p (the probability of “good” SMEs chosen to join the supply chain finance) in supply chain finance business, and the default cost is an important constrain for determining the strategies adopted by the SMEs and the banks. To achieve higher returns, SMEs and banks should make effects to create a better supply chain finance business environment to achieve the separation equilibrium.

Reporting bias in incomplete information model

2014 ◽  
Vol 123 (1) ◽  
pp. 45-49
Author(s):  
Maurice Peat ◽  
Jiri Svec ◽  
Jue Wang
Keyword(s):  
Incomplete Information ◽  
Information Model ◽  
Reporting Bias

scholarly journals Flip-Flopping, Primary Visibility, and the Selection of Candidates

2016 ◽  
Vol 8 (2) ◽  
pp. 61-85 ◽  
Author(s):  
Marina Agranov
Keyword(s):  
Incomplete Information ◽  
Detrimental Effect ◽  
Information Model ◽  
General Election ◽  
Primary Effect ◽  
Two Stage ◽  
Moderation Effect ◽  
General Elections ◽  
Selection Of

We present an incomplete information model of two-stage elections in which candidates can choose different platforms in primaries and general elections. Voters do not directly observe the chosen platforms, but infer the candidates' ideologies from observing candidates' campaigns. The ability of voters to detect candidates' types depends on the visibility of the race. This model captures two patterns: the post-primary moderation effect, in which candidates pander to the party base during the primary and shift to the center in the general election; and the divisive-primary effect, which refers to the detrimental effect of hard-fought primaries on a party's general-election prospects. (JEL D11, D72, D83)

scholarly journals Testing Models of Social Learning on Networks: Evidence From Two Experiments

Econometrica ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 1-32 ◽  
Author(s):  
Arun G. Chandrasekhar ◽  
Horacio Larreguy ◽  
Juan Pablo Xandri
Keyword(s):  
Social Learning ◽  
Incomplete Information ◽  
Information Model ◽  
Reduced Form ◽  
Form Analysis ◽  
Learning Agents ◽  
Lab Experiments ◽  
Binary State ◽  
Mixing Parameter ◽  
Learning Types

We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types—Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model; (2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced‐form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian‐villager and Mexican‐student samples, respectively.

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