Temačno prijateljstvo v Avstriji in Sloveniji

Young companies with growth opportunities face serious problems when it comes to financing. The private venture capital (VC) market fails to provide sufficient funding for this segment. First, we present the main characteristics of start-up companies and market failures that can lead to government intervention. These failures include asymmetric information embodied in the business plan; high transaction costs of the investment process from the investment decision to the exit; and positive externalities in the economy, as the government prefers other goals than profit realization. Government participation is categorized as direct or indirect intervention. We present international studies showing that indirect government intervention can have both beneficial and negative effects on the vc market. Finally, the Hungarian government’s participation and intervention are evaluated on the domestic vc market

A dynamic model of decision making in ATC: Adaptation of criterion across angle and time

Signal Detection Theory (SDT; Green & Swets 1966) is a well-established method for understanding performance on decision making tasks. Despite its popularity within the human factors community this method does not take into account the dynamic nature of decision making and the speed-accuracy tradeoffs that affect performance (Balakrishnan Busemeyer MacDonald & Lin 2003). This study tested a model of decision making that accounts for the dynamic processes affecting performance. Tested within the applied context of an Air Traffic Control conflict detection task the model provided a viable explanation of conflict decisions and decision times across a range of experimental conditions. At a practical level a successful model of conflict detection may inform the development and assessment of design attempts aimed at assisting controllers achieve optimal performance outcomes and reduce their workload. Copyright 2011 by Human Factors and Ergonomics Society, Inc. All rights reserved

Adaptive Decision Making in a Dynamic Environment: A Test of a Sequential Sampling Model of Relative Judgment

Research has identified a wide range of factors that influence performance in relative judgment tasks. However, the findings from this research have been inconsistent. Studies have varied with respect to the identification of causal variables and the perceptual and decision-making mechanisms underlying performance. Drawing on the ecological rationality approach, we present a theory of the judgment and decision-making processes involved in a relative judgment task that explains how people judge a stimulus and adapt their decision process to accommodate their own uncertainty associated with those judgments. Undergraduate participants performed a simulated air traffic control conflict detection task. Across two experiments, we systematically manipulated variables known to affect performance. In the first experiment, we manipulated the relative distances of aircraft to a common destination while holding aircraft speeds constant. In a follow-up experiment, we introduced a direct manipulation of relative speed. We then fit a sequential sampling model to the data, and used the best fitting parameters to infer the decision-making processes responsible for performance. Findings were consistent with the theory that people adapt to their own uncertainty by adjusting their criterion and the amount of time they take to collect evidence in order to make a more accurate decision. From a practical perspective, the paper demonstrates that one can use a sequential sampling model to understand performance in a dynamic environment, allowing one to make sense of and interpret complex patterns of empirical findings that would otherwise be difficult to interpret using standard statistical analyses

A sequential sampling account of response bias and speed-accuracy tradeoffs in a conflict detection task

Signal Detection Theory (SDT; Green & Swets, 1966) is a popular tool for understanding decision making. However, it does not account for the time taken to make a decision, nor why response bias might change over time. Sequential sampling models provide a way of accounting for speed-accuracy trade-offs and response bias shifts. In this study, we test the validity of a sequential sampling model of conflict detection in a simulated air traffic control task by assessing whether two of its key parameters respond to experimental manipulations in a theoretically consistent way. Through experimental instructions, we manipulated participants' response bias and the relative speed or accuracy of their responses. The sequential sampling model was able to replicate the trends in the conflict responses as well as response time across all conditions. Consistent with our predictions, manipulating response bias was associated primarily with changes in the model's Criterion parameter, whereas manipulating speed- accuracy instructions was associated with changes in the Threshold parameter. The success of the model in replicating the human data suggests we can use the parameters of the model to gain an insight into the underlying response bias and speed-accuracy preferences common to dynamic decision-making tasks

Evaluating the multi-conflict display

The growing demand for air travel in recent years has seen the development of new tools to help air traffic controllers manage their workload and continue to meet safety and performance standards [1]. However, studies have shown that these new tools do not always function as intended, and often result in poor performance outcomes [2]. To design better tools, designers must take greater consideration of the psychological processes involved in air traffic control [3, 4]. The Multi-Conflict Display [MCD; 5] was designed to help controllers by accounting for spatial-temporal processing involved in conflict detection. This paper accompanies a demonstration of the MCD and describes an intended evaluation to assess its effectiveness

Evidence accumulation in a complex task: making choices about concurrent multiattribute stimuli under time pressure

Evidence accumulation models transform observed choices and associated response times into psychologically meaningful constructs such as the strength of evidence and the degree of caution. Standard versions of these models were developed for rapid (∼1 s) choices about simple stimuli, and have recently been elaborated to some degree to address more complex stimuli and response methods. However, these elaborations can be difficult to use with designs and measurements typically encountered in complex applied settings. We test the applicability of 2 standard accumulation models—the diffusion (Ratcliff & McKoon, 2008) and the linear ballistic accumulation (LBA) (Brown & Heathcote, 2008)—to data from a task representative of many applied situations: the detection of heterogeneous multiattribute targets in a simulated unmanned aerial vehicle (UAV) operator task. Despite responses taking more than 2 s and complications added by realistic features, such as a complex target classification rule, interruptions from a simultaneous UAV navigation task, and time pressured choices about several concurrently present potential targets, these models performed well descriptively. They also provided a coherent psychological explanation of the effects of decision uncertainty and workload manipulations. Our results support the wider application of standard evidence accumulation models to applied decision-making settings