Causal analysis Actions

More spedfically, I shall argue that Marx himself offers an alternative framework that allows fora much more predseand fertile analysis. On this view, social science explanations are seen as three-tiered. First, there is a causal explanation of mental states, such as desires and beliefs (1.3.1). Next, there is intentional explanation of individual action in terms of the underlying beliefs and desires (1.2). Finally, there is causa) explanation of aggregate phenomena in terms of the individual actions that go into them. The last form is the specifically Marxist contribution to the methodology of the social sciences. I discuss it fust as a particular mode of causal analysis (1.3.2) and then again as a particular form of dialectical reasoning (1.5.3). 

In the threC tiered scheme of explanation suggested above, two varieties of causal analysis were involved. First, there are the causal explanations of preferences and other mental states, such as beliefs, emotions etc. I shall refer to this as explanation in terms of sub-intentional causality. Next, there are the causal explanations of aggregate social phenomena as the resultant outcome of many individual actions. This 1 refer to as supra-intentional causality. Metaphorically, the causal mechanisms involved in both cases can be said to operate "behind the back" of the individuals concerned. True, Marx uses that phrase only to refer to supra-intentional causality - the production of unintended consequences that thwart our efforts and subvert our aims. Yet the expression is equally apt as a characterization of the psychic causality that, unbeknown to the agent, shapes his beliefs and desires. Although Marx is best known for his study of supra-intentional causality, one aim of the present work is to argue that he was also a pioneer in the study of preference formation and - especially - belief formation. 

Each potentially hazardous control action must be considered. As an example, consider the unsafe control action of not turning off the power when the door is opened. Figure 8.7 shows the results of the causal analysis in a graphical form. Other ways of documenting the results are, of course, possible. 

Incidents and accidents are valued as an important window into systems that are not functioning as they should—triggering in-depth and uncircumscribed causal analysis and improvement actions. 

Employing Causal Analysis to identify the root factors behind observations then provides a solid basis for Corrective Action. 

This may help in eliminating the hazardous condition altogether, or to take actions to reduce its likelihood or frequency. The process is referred to as Causal Analysis. The Modelling methodology used in support of Causal Analysis is commonly known as Fault Tree Analysis. 

Therefore, asserts price dynamics are assumed to be stochastic processes. An early key-concept to understand stochastic processes was the random walk. The first theoretical description of a random walk in the natural sciences was performed in 1905 by Einstein s analysis of molecular interactions. But the first mathematization of a random walk was not realized in physics, but in social sciences by the French mathematician, Louis Jean Bachelier (1870-1946). In 1900 he published his doctoral thesis with the title Theorie de la Spdculation [28]. During that time, most market analysis looked at stock and bond prices in a causal way Something happens as cause and prices react as effect. In complex markets with thousands of actions and reactions, a causal analysis is even difficult to work out afterwards, but impossible to forecast beforehand. One can never know everything. Instead, Bachelier tried to estimate the odds that prices will move. He was inspired by an analogy between the diffusion of heat through a substance and how a bond price wanders up and down. In his view, both are processes that cannot be forecast precisely. At the level of particles in matter or of individuals in markets, the details are too complicated. One... 

In the second case study, variation tree analysis and the events and causal factors chart/root cause analysis method are applied to an incident in a resin plant. This case study illustrates the application of retrospective analysis methods to identify the imderlying causes of an incident and to prescribe remedial actions. This approach is one of the recommended strategies in the overall error management framework described in Chapter 8. 

The tool helps the investigator to understand and focus on the failed harriers, which are normally identified as causal factors. These failed harriers may need to he strengthened, replaced, or supplemented, especially where weak administrative controls are highlighted. Even successful barriers that prevented more serious consequences may require reinforcement. Therefore, barrier analysis can give the investigator valuable insights into how the incident happened and some of the multiple causes that need corrective action to prevent recurrence. 

Change analysis o) (also known as Change Evaluation/Analysis, CE/A) is another tool that can assist the identification of causal factors. It is useful for brainstorming about what has changed since conditions were safe, or perceived as safe. It may also be used for hypothesizing potential contributory factors to a hazardous condition or action. 

The committee s analysis was conducted on several levels. First, members investigated the causal factors for each of the seven events listed in Tables 2-1 and 2-2. They then developed a notional causal tree for each of the two events in Table 2-2 that were analyzed in depth. For illustrative purposes, a causal tree developed by the committee for the December 3-5,2000, incident at JACADS appears at Appendix F. The tree is a standard tool in reliability analysis and is particularly useful in human reliability analysis where operator actions contribute either positively or negatively to an incident. Lastly, the committee provides a series of general and specific observations about the events. 

If hazard identification and analysis do not relate to actual causal factors, corrective actions will be misdirected and ineffective. 

If hazard identification and analysis do not relate to actual causal factors, the resulting corrective actions proposed will be misdirected and ineffective. A superior quality of incident investigation is required to identify and evaluate actual causal factors so that appropriate corrective actions can be taken. 

A criticism of historical, after-the-fact data (of outcome statistics) is that such measures are not hazard-specific that is, they do not identify incident causal factors without additional analysis. That s so. If safety professionals want to identify hazard-specific situations that may be predictive and give direction to the actions that should be taken to reduce risk, they will have to do some analysis. 

Improvement analysis relates very much to analyzing the direct and indirect effects of maintenance actions, i.e. what are the resources spent (time, people, money, materials), and what is the output in terms of failure rates, safety etc. However, we encounter some challenges in this process, e.g. lack of good empiric causal relations between the performed maintenance and the resulting failure rate, and that the vast numbers of components - each of them being less important , makes e.g. condition monitoring activities a trade-off between volume and accuracy. 

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