101 research outputs found
Improved Program Planning Generates Large Benefits in High Risk Crop Farming – A Profitable Application of Time Series Models and Stochastic Optimization
No full textAgricultural production relies to a great extent on biological processes in natural environments. In addition to volatile prices, it is thus heavily exposed to risks caused by the variability of natural conditions such as rainfall, temperature and pests. With a view to the apparently lacking support of risky farm production program decisions through formal planning models, the objective of this paper is to examine whether, and eventually by how much, farmers’ “intuitive” program decisions can be improved through formal statistical analyses and stochastic optimization models. In this performance comparison, we use the results of the formal planning approach that are generated in a quasi ex-ante analysis as a normative benchmark for the empirically observed ones. To avoid benchmark solutions that would possibly exceed the respective farmer’s risk tolerance, we limit the formal search to a subset of solutions that are second-degree stochastically dominant compared to the farmer’s own decision. We furthermore compare the suitability of different statistical (time series) models to forecast the uncertainty of single gross margins.stochastic optimization, program planning, time series analysis, Crop Production/Industries,
The slowdown in German bank lending - revisited
No full textThe rate of growth in bank loans to private households and firms in Germany has declined substantially since early 2000 and currently stands at virtually zero. In this article, we analyse whether cyclical factors (demand-side driven) or banks unwillingness and/or inability to lend (supply-side driven) can be held responsible for this trend.Our preliminary results suggest that the slowdown in bank loan expansion is largely driven by a decline in the demand for loans. This result is supported by taking into account the latest tendency of corporates substituting bank loans for the issuance of money and capital market instruments. Although it cannot be ruled out that supply-side restrictions have contributed to the dampening of real bank loan expansion, to date these factors have played only a minor role. --German bank lending,Credit rationing
Urban park soundscapes and their perceived restorativeness
No full textIndividual sounds and soundscapes can influence individuals, their place evaluations and
potentially their psychological restoration. As urban park soundscapes can vary greatly, ranging
from quiet, serene oases to noisy city spaces, it is important to understand how they are perceived
and evaluated, as they could influence people’s experience and evaluation of the park in general.
This paper studies the different types of soundscapes that are perceived in urban parks and
examines if these soundscapes vary in their perceived restorativeness
The Uncontrolled Social Utility Hypothesis Revisited
No full textThe experiment disentangles communication and social effect in face−to−face communication. The results question the previous interpretation of communication effects in ultimatum bargaining, and suggest that separate processes, both of a strategic and of an affective−social nature induce cooperative outcomes.
Author contributions: L.F
No full textIt is well-known that financial asset returns exhibit fat-tailed distributions and long-term memory. These empirical features are the main objectives of modeling efforts using (i) stochastic processes to quantitatively reproduce these features and (ii) agent-based simulations to understand the underlying microscopic interactions. After reviewing selected empirical and theoretical evidence documenting the behavior of traders, we construct an agent-based model to quantitatively demonstrate that "fat" tails in return distributions arise when traders share similar technical trading strategies and decisions. Extending our behavioral model to a stochastic model, we derive and explain a set of quantitative scaling relations of long-term memory from the empirical behavior of individual market participants. Our analysis provides a behavioral interpretation of the long-term memory of absolute and squared price returns: They are directly linked to the way investors evaluate their investments by applying technical strategies at different investment horizons, and this quantitative relationship is in agreement with empirical findings. Our approach provides a possible behavioral explanation for stochastic models for financial systems in general and provides a method to parameterize such models from market data rather than from statistical fitting. complex systems | power law | scaling laws M odeling price returns has become a central topic in the study of financial markets due to its key role in financial theory and its practical utility. Following models by Engle and Bollerslev (1, 2), many stochastic models have been proposed based on statistical studies of financial data to accurately reproduce price dynamics. In contrast to this stochastic approach, economists and physicists using the tools of statistical mechanics have adopted a bottom-up approach to simulate the same macroscopic regularity of price changes, with a focus on the behavior of individual market participants (3-10). Although the second socalled agent-based approach has provided a qualitative understanding of price mechanisms, it has not yet achieved sufficient quantitative accuracy to be widely accepted by practitioners. Here, we combine the agent-based approach with the stochastic process approach and propose a model based on the empirically proven behavior of individual market participants that quantitatively reproduces fat-tailed return distributions and long-term memory properties (11-14). Empirical and Theoretical Market Behaviors We start by arguing that technical traders (usually agents seeking arbitrage opportunities and make their trading decisions based on price patterns) contribute much more to the dynamics of daily stock prices S t (or log price s t ≡ lnðS t Þ) than fundamentalists (who attempt to determine the fundamental values of stocks). Although fundamentalists hold a majority of the stocks, they trade infrequently (see SI Appendix, Market surveys (16-18) also provide clear evidence of the prevalence of technical analysis. We consider here only technical traders, assuming that fundamentalists contribute only to market noise. Our study is of the empirical data recorded prior to 2006 and ignores the effect of high frequency trading (HFT) that has become significant only in the past 5 y. We propose a behavioral agent-based model that is in agreement with the following empirical evidence: i. Random trading decisions made by agents on a daily basis. n 0 technical traders use different trading strategies, hence their decisions to buy, sell, or hold a position appear to be random. A trading decision is made daily because empirical studies report the lack of intraday trading persistence in empirical trading data (19). Market survey (16) also shows that fund managers put very little emphasis on intraday tradings. We estimate the probability p of having daily trade empirically from trading volumes. ii. Price returns. The price return r t ≡ s t − s t−1 is controlled by the imbalance d t between the demand and the supply of stocks-the difference in the number of buy and sell trades each day. The excess in total demand or supply moves the price up or down, where the largest r t occurs when all traders act in unison, when they all either buy or sell their stocks. We assume this relationship between price change r t and d t to be linear each day, as supported by empirical findings (20, 21). iii. Centralized interaction mechanism of returns on technical strategies. For technical traders, an important input parameter in their strategies is past price movement (22, 23). Consequently, prices and orders reflect a main interaction mechanism between agents. In many agent-based models, the interaction strength between agents need to be adjusted with agent population size (5, 24, 25) or interaction structure (26) to sustain "fat" tails in return distributions. Here we propose a centralized interaction mechanism (price change) among agents so that the strength of interaction grows with agent population and is unaffected by interaction structure. iv. Opinion convergence due to price changes. This is the unique mechanism that distinguishes our model from other models. It specifies the collective behavior of technical traders. Duffy et al. (27) found that agents learn from each other and tend to adopt the strategy that gives the most payoff. Given the price patterns at any point in time, a few most profitable technical strategies dominate the market because every technical trade
Measuring the perceived restorativeness of soundscapes : is it about the sounds, the person, or the environment?
No full textTo determine the ‘restorative aspects of sound exposure’ a reliable and valid
measure is needed. A Perceived Restorativeness Soundscape Scale (PRSS),
which measures the level of Fascination, Being-Away, Compatibility, and
Extent (FACE), has been proposed and shown to be reliable. This study aimed
to test its validity further by establishing the comprehension and
interpretation of the scale’s items. Ten participants completed a
questionnaire involving adapted items of the PRSS. Half the questions were
phrased in relation to the soundscape (holistic), the other, near, identical
half were phrased in relation to the sounds (specific). Participants rated their
agreement with each item using a 7 point Likert scale and wrote the reason
for their response. A semi-structured interview followed the questionnaire,
which took place in two urban cafés. The question framing (holistic or
specific) did not result in varied responses for these matched items. However,
depending on the FACE component being measured responses varied in
their reference to a) the place, soundscape, or individual sounds, and b)
the individual’s moods and desires, or the temporality of the
sound(scape). Increased understanding of FACE components and amendments
to the PRSS are necessary to improve the scale’s comprehension and validity
Control and Filtering for Discrete Linear Repetitive Processes with H infty and ell 2--ell infty Performance
No full textRepetitive processes are characterized by a series of sweeps, termed passes, through a set of dynamics defined over a finite duration known as the pass length. On each pass an output, termed the pass profile, is produced which acts as a forcing function on, and hence contributes to, the dynamics of the next pass profile. This can lead to oscillations which increase in amplitude in the pass to pass direction and cannot be controlled by standard control laws. Here we give new results on the design of physically based control laws for the sub-class of so-called discrete linear repetitive processes which arise in applications areas such as iterative learning control. The main contribution is to show how control law design can be undertaken within the framework of a general robust filtering problem with guaranteed levels of performance. In particular, we develop algorithms for the design of an H? and dynamic output feedback controller and filter which guarantees that the resulting controlled (filtering error) process, respectively, is stable along the pass and has prescribed disturbance attenuation performance as measured by and – norms
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