scholarly journals Critical Discount Factor Values in Discounted Supergames

Games ◽  
2018 ◽  
Vol 9 (3) ◽  
pp. 47 ◽  
Author(s):  
Kimmo Berg ◽  
Markus Kärki
Keyword(s):  
Discount Factor ◽  
Critical Discount Factor

The Influence of Product Quality Differentiation on Telecom Carriers' Collusion

2020 ◽  
Vol 12 (2) ◽  
pp. 19-35
Author(s):  
Meijuan Li ◽  
Qiming Tang
Keyword(s):  
Product Quality ◽  
Discount Factor ◽  
Game Model ◽  
Quality Differentiation ◽  
Service Operation ◽  
Telecom Industry ◽  
The Government ◽  
Critical Discount Factor ◽  
Telecom Carriers

The telecom industry is a typical oligopolistic industry, so telecom carriers are easy to conspire in the course of operation. This article analyzes the influence of product quality differentiation on telecom carrier collusion by constructing a game model. The results show that the critical discount factor that keeps telecom carriers persisting in collusion increases with the decrease of product quality differentiation, that is, the smaller the product quality differentiation is, the harder it is for telecom carriers to adhere to long-term collusion. Conversely, the greater the product quality differentiation is, the greater the risk of a large number of users leaving the network can be when telecom carriers betray the collusion, so they will not easily betray it. Therefore, the product quality differentiation is conducive to the realization and maintenance of telecom carrier collusion. At the same time, telecom carriers that provide different quality products have different incentives for collusion, among which those providing low-quality products are more likely to persist in collusion. What is more, this article also finds that China's telecom industry has great product quality differentiation in the full-service operation, so when telecom carriers pursue the differentiation development of product quality, the government should take corresponding measures to prevent their collusion.

The Effect of T-Commerce Scarcity Messages and Price Policies on Attitudes and Purchase Intention

2019 ◽  
Vol 118 (1) ◽  
pp. 42-47
Author(s):  
KwangSeok Han
Keyword(s):  
Statistical Analysis ◽  
Purchase Intention ◽  
Time Limit ◽  
Discount Factor ◽  
Limited Time ◽  
Price Discount ◽  
Discount Price ◽  
Strategic Plans ◽  
Factor Design ◽  
The Difference

Background/Objectives: This study investigated differences in the attitude of users according to type of scarcity message and price discount conditions to compose T-commerce sales messages and search for effective strategic plans. Methods/Statistical analysis: This study empirically verifies the difference in promotion attitude and purchase intention between the type of T-Commerce scarcity message (quantity limit message / time limit message) and the price discount policy (price discount / non-discount) message. For this purpose, 2 (scarcity type: limited quantity, limited time) X 2 (with or without price discount: price discount, no price discount) factor design between subjects was used.

Time Preferences across Language Groups: Evidence on Intertemporal Choices from the Swiss Language Border*

2021 ◽  
Author(s):  
Holger Herz ◽  
Martin Huber ◽  
Tjaša Maillard-Bjedov ◽  
Svitlana Tyahlo
Keyword(s):  
Hyperbolic Discounting ◽  
Time Preferences ◽  
Discount Factor ◽  
Socioeconomic Conditions ◽  
Measuring Time ◽  
Present Bias ◽  
Language Groups ◽  
Long Run ◽  
Intertemporal Choices

Abstract Differences in patience across language groups have recently received increased attention in the literature. We provide evidence on this issue by measuring time preferences of French and German speakers from a bilingual municipality in Switzerland where institutions are shared and socioeconomic conditions are very similar across the two language groups. We find that French speakers are significantly more impatient than German speakers, and differences are particularly pronounced when payments in the present are involved. Estimates of preference parameters of a quasi-hyperbolic discounting model suggest significant differences in both present bias (β) and the long-run discount factor (δ) across language groups.

scholarly journals Non Stationary Multi-Armed Bandit: Empirical Evaluation of a New Concept Drift-Aware Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 380
Author(s):  
Emanuele Cavenaghi ◽  
Gabriele Sottocornola ◽  
Fabio Stella ◽  
Markus Zanker
Keyword(s):  
Real World ◽  
Concept Drift ◽  
Empirical Evaluation ◽  
Sliding Window ◽  
Discount Factor ◽  
Data Streaming ◽  
Sources Of Information ◽  
Sequential Decision ◽  
Time Step ◽  
Thompson Sampling

The Multi-Armed Bandit (MAB) problem has been extensively studied in order to address real-world challenges related to sequential decision making. In this setting, an agent selects the best action to be performed at time-step t, based on the past rewards received by the environment. This formulation implicitly assumes that the expected payoff for each action is kept stationary by the environment through time. Nevertheless, in many real-world applications this assumption does not hold and the agent has to face a non-stationary environment, that is, with a changing reward distribution. Thus, we present a new MAB algorithm, named f-Discounted-Sliding-Window Thompson Sampling (f-dsw TS), for non-stationary environments, that is, when the data streaming is affected by concept drift. The f-dsw TS algorithm is based on Thompson Sampling (TS) and exploits a discount factor on the reward history and an arm-related sliding window to contrast concept drift in non-stationary environments. We investigate how to combine these two sources of information, namely the discount factor and the sliding window, by means of an aggregation function f(.). In particular, we proposed a pessimistic (f=min), an optimistic (f=max), as well as an averaged (f=mean) version of the f-dsw TS algorithm. A rich set of numerical experiments is performed to evaluate the f-dsw TS algorithm compared to both stationary and non-stationary state-of-the-art TS baselines. We exploited synthetic environments (both randomly-generated and controlled) to test the MAB algorithms under different types of drift, that is, sudden/abrupt, incremental, gradual and increasing/decreasing drift. Furthermore, we adapt four real-world active learning tasks to our framework—a prediction task on crimes in the city of Baltimore, a classification task on insects species, a recommendation task on local web-news, and a time-series analysis on microbial organisms in the tropical air ecosystem. The f-dsw TS approach emerges as the best performing MAB algorithm. At least one of the versions of f-dsw TS performs better than the baselines in synthetic environments, proving the robustness of f-dsw TS under different concept drift types. Moreover, the pessimistic version (f=min) results as the most effective in all real-world tasks.

Hedge Fund Strategies, PerformanceDiversification: A Portfolio Theory & Stochastic Discount Factor Approach

2021 ◽  
pp. 101000
Author(s):  
David Newton ◽  
Emmanouil Platanakis ◽  
Dimitrios Stafylas ◽  
Charles Sutcliffe ◽  
Xiaoxia Ye
Keyword(s):  
Hedge Fund ◽  
Portfolio Theory ◽  
Discount Factor ◽  
Stochastic Discount Factor

The Stackelberg Games of Water Extraction with Myopic Agents

2021 ◽  
Author(s):  
Alain Jean-Marie ◽  
Mabel Tidball ◽  
Víctor Bucarey López
Keyword(s):  
Stackelberg Game ◽  
Infinite Horizon ◽  
Dynamic Game ◽  
Water Extraction ◽  
Discount Factor ◽  
Stackelberg Games ◽  
Groundwater Extraction ◽  
Strategic Interactions ◽  
Water Users ◽  
Threshold Policies

We consider a discrete-time, infinite-horizon dynamic game of groundwater extraction. A Water Agency charges an extraction cost to water users and controls the marginal extraction cost so that it depends not only on the level of groundwater but also on total water extraction (through a parameter [Formula: see text] that represents the degree of strategic interactions between water users) and on rainfall (through parameter [Formula: see text]). The water users are selfish and myopic, and the goal of the agency is to give them incentives so as to improve their total discounted welfare. We look at this problem in several situations. In the first situation, the parameters [Formula: see text] and [Formula: see text] are considered to be fixed over time. The first result shows that when the Water Agency is patient (the discount factor tends to 1), the optimal marginal extraction cost asks for strategic interactions between agents. The contrary holds for a discount factor near 0. In a second situation, we look at the dynamic Stackelberg game where the Agency decides at each time what cost parameter they must announce. We study theoretically and numerically the solution to this problem. Simulations illustrate the possibility that threshold policies are good candidates for optimal policies.

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