Sex hormonal modulation of hyperbolic discount factor in men.

2008 ◽  
Vol 1 (1) ◽  
pp. 7-16 ◽  
Author(s):  
Taiki Takahashi ◽  
Kikue Sakaguchi ◽  
Mariko Oki ◽  
Toshikazu Hasegawa
Keyword(s):  
Discount Factor ◽  
Hyperbolic Discount ◽  
Hormonal Modulation

Hormonal modulation of ishikawa cells during three-dimensional growth in vitro☆

1998 ◽  
Vol 5 (4) ◽  
pp. 217-223 ◽  
Author(s):  
D PINELLI ◽  
J DRAKE ◽  
M WILLIAMS ◽  
D CAVANAGH ◽  
J BECKER
Keyword(s):  
Three Dimensional ◽  
Ishikawa Cells ◽  
Hormonal Modulation ◽  
Dimensional Growth

Subjective Time Preference and Willingness to Pay for an Energy-Saving Durable Good

2001 ◽  
Vol 32 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Gerrit Antonides ◽  
Sophia R. Wunderink
Keyword(s):  
Willingness To Pay ◽  
Energy Saving ◽  
Subjective Time ◽  
Durable Good ◽  
Discount Function ◽  
Hyperbolic Discount ◽  
The One ◽  
Two Parameter ◽  
Difference Effect ◽  
Better Than

Summary: Different shapes of individual subjective discount functions were compared using real measures of willingness to accept future monetary outcomes in an experiment. The two-parameter hyperbolic discount function described the data better than three alternative one-parameter discount functions. However, the hyperbolic discount functions did not explain the common difference effect better than the classical discount function. Discount functions were also estimated from survey data of Dutch households who reported their willingness to postpone positive and negative amounts. Future positive amounts were discounted more than future negative amounts and smaller amounts were discounted more than larger amounts. Furthermore, younger people discounted more than older people. Finally, discount functions were used in explaining consumers' willingness to pay for an energy-saving durable good. In this case, the two-parameter discount model could not be estimated and the one-parameter models did not differ significantly in explaining the data.

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 Hormonal modulation of ESR1 mutant metastasis

Oncogene ◽  
2020 ◽  
Author(s):  
Guowei Gu ◽  
Lin Tian ◽  
Sarah K. Herzog ◽  
Yassine Rechoum ◽  
Luca Gelsomino ◽  
...  
Keyword(s):  
Hormonal Modulation

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
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