1,721,124 research outputs found
A generative decision theory. An agent-based computational approach for modelling, studying and making decisions in organizations
No full textIn recent years organizations have improved their abilities to study and predict possible situations thanks to new management techniques and to the development of technological instruments capable of capturing and analyzing large volumes of data. In a system where organizational variables are becoming more and more con-trolled and where the main actor still remains the most important unknown factor: humans and their behavior, their decision-making processes are more and more crucial for organizations and their stability.
Several business and social science fields such as management, economics and psychology in the last decades have identified better how human habits and decision-making processes are connected. Human’s behavior has been studied through the development of decision science. In particular, two approaches have been used for studying, modeling and making decisions. Which are: the normative and the descriptive approach. The normative approach is based on the analysis of decisions related to simple rules and norms used by a hypothetical human that make decisions. The descriptive approach, ex-amines individual decisions in the context of a set of needs, preferences, beliefs and values that an individual has.
Considering the first approach, most human characteristics have been smooth out to create sophisticated simulations. Individuals are programmed to choose the best rational strategy and capable of maximizing the utility. Basically, this approach considers an ideal decision maker whom is fully informed, completely rational, and able to compute with perfect accuracy. The second approach named descriptive, aims to study people’s behavior starting from a basic decision level, in a very accurate way but often without computing a theory. Because many variables are required to predict people’s behavior. Although, the descriptive decision theory has demonstrated how the normative theory has a lack of understanding for some traits of real human behavior.
The Prospect theory of Daniel Kahneman and Amos Tversky is probably the most well-known example. Kahneman and Tversky identified three regularities in human decision-making, which are that: people put more emphasis on adjustments in their utility-states than focusing on absolute utilities; losses are perceived as bigger than profits; and the evaluation of subjective odds is biased by a sort of anchoring effect when choosing.
As for these empirical evidences, descriptive approach presents several other decision phenomena named heuristics and biases, which are human strategies and departures from classic normative rationality, that have a relevant impact on human decision making processes. These studies have proved that humans make decisions differently compare to the normative theory. This demonstrates the limits of this approach. On the other hand, studies developed by the descriptive approach, difficulty lead to new models that are able to predict human behavior because of the high level of analysis of the research. Limits of both approaches helped from one side to develop some normative models which consider more human habits. On the other side, the descriptive studies started by analyzing trend’s choices in order to model decision processes. Evolution in decision study approaches has allowed the development of some applicative studies, for example in the medical sector, engineering and especially in Economics and Organizational studies. This was the major factor in the emergence of behavioral economics, earning Kahneman a Nobel Prize in 2002.
At the proof of interest for the heuristic and bias program, over time, studies from cited disciplines have demonstrated that subjects infringe on many other axioms of rationality in different applied fields, by detecting the presence of numerous biases. On the other hand, productivity in heuristics and biases has become a double-edged sword. The program in biases and heuristics led to a sort of rush in discovering new biases and heuristics, in several cases very similar to each other.
Some researchers thought that it was possible to reorganize this fragmented research program by introducing classifications and taxonomies of cognitive phenomena. The scientific literature presents some examples of classifications and taxonomies of heuristics and biases based on different theoretical approaches. Currently, the absence of common criteria of categorizations makes the process of comparison between biases very difficult. Recent studies suggest an empirical approach to realize a taxonomy, based on experimental research in decision-making which has shown the presence of variability among different individuals in their abilities to solve problems that were created to identify cognitive fallacies.
The first part of the dissertation will present a series of evidence on relations between several biases and heuristics, to highlight the presence of underling factors and dimensions of origin of these cognitive departures. Based on these results, researchers thought that departures from normative standards could be due to more random performance errors, rather than consistent fallacies across decision-making skills.
The idea that heuristics and biases are driven by different mental strategies is also known by evolutionary psychologists but they con-sider the “heuristics and biases program” denigrating for human reasoning because it is described as fallacious instead of being an evolutionary resource. In particular, Gerd Gigerenzer has described heuristics as processes that help us make better choices and economize cognitive resources rather than just departures from rationality. He considers heuristics as efficient cognitive processes that ignore information in order to make better decisions in a limited amount of time.
Finding a common definition for different approaches for biases and heuristics, some researchers consider them as all that can differentiate us as humans, in the decision making processes, compared to a normative rational agent, like a computer. This definition could be acceptable if these differences are considered as a process exclusively present in human nature, and impossible to be emulated by a ration-al agent. Like a computer for example.
It might seem bizarre as a response, but during the same period when the descriptive approach was rising and showing all the limits of the normative approach, a new paradigm of studying and doing science was emerging: it is called the generative approach.
As seen the descriptive approach is based on phenomena observation and of the deduction thinking process based on it. The observation method is related to experimental work and laboratory studies of individual decision-making processes. It has the merit of improving a real picture of how humans make decisions, compared to the normative method.
However, even a perfect knowledge of individual decision-making rules does not guaranty the possibility of predicting the macroscopic structure of human behavior. This possibility has been lately explored in recent years, starting from some solid scientific studies on individual behavior and using advance computing techniques capable of “growing up" phenomena at the macro level, making it possible to obtain counterintuitive hypothesis about behavior and implications in organizations. This method is definitively powerful for testing, with generative sufficiency and some unexpected rules given by behavioral research.
The method can be considered as a scientific revolution, according to some researchers, it is a third way of doing science, after deduction and induction methods. The generative method consists of generating and growing a phenomenon for explaining it. In recent years, computer simulation of social phenomena has produced a new scientific paradigm, which is the science that generates events and that would be impossible to recreate them or observe them. Computer simulation based on virtual agents (ABM) has become an essential tool to generate observable facts, an instrument of revolutionary development for the decision science. ABM offers an innovative and effective manner to conduct empirical research. ABM purposes to create considerable social phenomena qualitatively and quantitatively. Literature provides numerous models of prosocial behavior, cooperation, punishment, organization dynamics, and social phenomena. In fact, one of the possibilities offered by this tool is to explore behavioral effects at a macro level, such as the organizational level. Several organizations have already started using this approach to study the consequences of policies of the behavior of the individuals who com-pose them. The approach has given excellent results in terms of simulations enabling the study of effects of decisions and helping to make good organizational decisions. In particular a characteristic of the ABM instrument, and more generally the generative science, is the use of information from different approaches (descriptive and normative) to create dynamics that would not be possible otherwise.
In this dissertation, in order to show the importance of this instrument, ABMs studies have been developed based on results obtained from the empirical results of experiments developed in the first part of the research. These studies have been realized through an implementation into agents of cognitive fallacies, biases and heuristics in order to study decisional processes and effects on organizations and artificial societies of belonging.
The present dissertation will present step by step this innovative method that has revolutionized various sciences, in this case the decision sciences applied to organizational contexts. Because several disciplines are involved in this dissertation, such as: decisional sciences, organizational studies and artificial intelligence, it will present some theoretical parts regarding the scope of the study. First, the study of decision-making related to decisions within the organization will be introduced, and then more general theories of decisions will be presented. Then, it will introduce the generative method applied to the decisions and the ABM as the instrument for excellence for studying decisions in organizations.In recent years organizations have improved their abilities to study and predict possible situations thanks to new management techniques and to the development of technological instruments capable of capturing and analyzing large volumes of data. In a system where organizational variables are becoming more and more con-trolled and where the main actor still remains the most important unknown factor: humans and their behavior, their decision-making processes are more and more crucial for organizations and their stability.
Several business and social science fields such as management, economics and psychology in the last decades have identified better how human habits and decision-making processes are connected. Human’s behavior has been studied through the development of decision science. In particular, two approaches have been used for studying, modeling and making decisions. Which are: the normative and the descriptive approach. The normative approach is based on the analysis of decisions related to simple rules and norms used by a hypothetical human that make decisions. The descriptive approach, ex-amines individual decisions in the context of a set of needs, preferences, beliefs and values that an individual has.
Considering the first approach, most human characteristics have been smooth out to create sophisticated simulations. Individuals are programmed to choose the best rational strategy and capable of maximizing the utility. Basically, this approach considers an ideal decision maker whom is fully informed, completely rational, and able to compute with perfect accuracy. The second approach named descriptive, aims to study people’s behavior starting from a basic decision level, in a very accurate way but often without computing a theory. Because many variables are required to predict people’s behavior. Although, the descriptive decision theory has demonstrated how the normative theory has a lack of understanding for some traits of real human behavior.
The Prospect theory of Daniel Kahneman and Amos Tversky is probably the most well-known example. Kahneman and Tversky identified three regularities in human decision-making, which are that: people put more emphasis on adjustments in their utility-states than focusing on absolute utilities; losses are perceived as bigger than profits; and the evaluation of subjective odds is biased by a sort of anchoring effect when choosing.
As for these empirical evidences, descriptive approach presents several other decision phenomena named heuristics and biases, which are human strategies and departures from classic normative rationality, that have a relevant impact on human decision making processes. These studies have proved that humans make decisions differently compare to the normative theory. This demonstrates the limits of this approach. On the other hand, studies developed by the descriptive approach, difficulty lead to new models that are able to predict human behavior because of the high level of analysis of the research. Limits of both approaches helped from one side to develop some normative models which consider more human habits. On the other side, the descriptive studies started by analyzing trend’s choices in order to model decision processes. Evolution in decision study approaches has allowed the development of some applicative studies, for example in the medical sector, engineering and especially in Economics and Organizational studies. This was the major factor in the emergence of behavioral economics, earning Kahneman a Nobel Prize in 2002.
At the proof of interest for the heuristic and bias program, over time, studies from cited disciplines have demonstrated that subjects infringe on many other axioms of rationality in different applied fields, by detecting the presence of numerous biases. On the other hand, productivity in heuristics and biases has become a double-edged sword. The program in biases and heuristics led to a sort of rush in discovering new biases and heuristics, in several cases very similar to each other.
Some researchers thought that it was possible to reorganize this fragmented research program by introducing classifications and taxonomies of cognitive phenomena. The scientific literature presents some examples of classifications and taxonomies of heuristics and biases based on different theoretical approaches. Currently, the absence of common criteria of categorizations makes the process of comparison between biases very difficult. Recent studies suggest an empirical approach to realize a taxonomy, based on experimental research in decision-making which has shown the presence of variability among different individuals in their abilities to solve problems that were created to identify cognitive fallacies.
The first part of the dissertation will present a series of evidence on relations between several biases and heuristics, to highlight the presence of underling factors and dimensions of origin of these cognitive departures. Based on these results, researchers thought that departures from normative standards could be due to more random performance errors, rather than consistent fallacies across decision-making skills.
The idea that heuristics and biases are driven by different mental strategies is also known by evolutionary psychologists but they con-sider the “heuristics and biases program” denigrating for human reasoning because it is described as fallacious instead of being an evolutionary resource. In particular, Gerd Gigerenzer has described heuristics as processes that help us make better choices and economize cognitive resources rather than just departures from rationality. He considers heuristics as efficient cognitive processes that ignore information in order to make better decisions in a limited amount of time.
Finding a common definition for different approaches for biases and heuristics, some researchers consider them as all that can differentiate us as humans, in the decision making processes, compared to a normative rational agent, like a computer. This definition could be acceptable if these differences are considered as a process exclusively present in human nature, and impossible to be emulated by a ration-al agent. Like a computer for example.
It might seem bizarre as a response, but during the same period when the descriptive approach was rising and showing all the limits of the normative approach, a new paradigm of studying and doing science was emerging: it is called the generative approach.
As seen the descriptive approach is based on phenomena observation and of the deduction thinking process based on it. The observation method is related to experimental work and laboratory studies of individual decision-making processes. It has the merit of improving a real picture of how humans make decisions, compared to the normative method.
However, even a perfect knowledge of individual decision-making rules does not guaranty the possibility of predicting the macroscopic structure of human behavior. This possibility has been lately explored in recent years, starting from some solid scientific studies on individual behavior and using advance computing techniques capable of “growing up" phenomena at the macro level, making it possible to obtain counterintuitive hypothesis about behavior and implications in organizations. This method is definitively powerful for testing, with generative sufficiency and some unexpected rules given by behavioral research.
The method can be considered as a scientific revolution, according to some researchers, it is a third way of doing science, after deduction and induction methods. The generative method consists of generating and growing a phenomenon for explaining it. In recent years, computer simulation of social phenomena has produced a new scientific paradigm, which is the science that generates events and that would be impossible to recreate them or observe them. Computer simulation based on virtual agents (ABM) has become an essential tool to generate observable facts, an instrument of revolutionary development for the decision science. ABM offers an innovative and effective manner to conduct empirical research. ABM purposes to create considerable social phenomena qualitatively and quantitatively. Literature provides numerous models of prosocial behavior, cooperation, punishment, organization dynamics, and social phenomena. In fact, one of the possibilities offered by this tool is to explore behavioral effects at a macro level, such as the organizational level. Several organizations have already started using this approach to study the consequences of policies of the behavior of the individuals who com-pose them. The approach has given excellent results in terms of simulations enabling the study of effects of decisions and helping to make good organizational decisions. In particular a characteristic of the ABM instrument, and more generally the generative science, is the use of information from different approaches (descriptive and normative) to create dynamics that would not be possible otherwise.
In this dissertation, in order to show the importance of this instrument, ABMs studies have been developed based on results obtained from the empirical results of experiments developed in the first part of the research. These studies have been realized through an implementation into agents of cognitive fallacies, biases and heuristics in order to study decisional processes and effects on organizations and artificial societies of belonging.
The present dissertation will present step by step this innovative method that has revolutionized various sciences, in this case the decision sciences applied to organizational contexts. Because several disciplines are involved in this dissertation, such as: decisional sciences, organizational studies and artificial intelligence, it will present some theoretical parts regarding the scope of the study. First, the study of decision-making related to decisions within the organization will be introduced, and then more general theories of decisions will be presented. Then, it will introduce the generative method applied to the decisions and the ABM as the instrument for excellence for studying decisions in organizations
Assessment and development centers: judgement biases and risks of using idiographic and nomothetic approaches to collecting information on people to be evaluated and trained in organizations
No full textAssessment center and development center are two procedures that organizations can use in order to evaluate and train people. They make use of different methods and techniques, some (i.e. interviews) descending from the so called idiographic (or clinical) approach, and some (i.e. standardized instruments) descending from the so called nomothetic (or psychometric) approach. The idea is that different methods and techniques allow assessors and decision makers to collect as much information as possible, in order to come to an integrated judgment of people to be evaluated. Regarding this idea, psychological research has already discovered that it is not the amount of information collected that makes the difference between expert and non-expert assessors and decision makers. Besides, too much information is difficult to manage; and while it increases the confidence of assessors and decision makers about their judgments, it unfortunately does not increase their accuracy as well, since relevant information is mixed with irrelevant one and this makes it difficult to decide which one to consider and which one not. So, the article wants to be a critical review of what psychological science has found, and not so recently, in the field of assessment and development of psychological characteristics, in terms of risks and biases. Finally, it wants to underline the fact that, in spite of risks and biases, nowadays different methods and techniques are actually used to assess one person’s psychological characteristics, which is certainly questionable but also methodologically appropriate if they are appropriately used
Orientamento e crisi economica: la ricerca-intervento con le classi terze di un istituto tecnico per geometri del Nord Italia
Full text linkThe article reports the example of an action-research carried out in 2011 for educational guidance purposes with the third-year students from a Technical School for Surveyors in North Italy. 184 students took part in the action-research, 22.1% females, 77.9% males, from 16 (modal age) to 18 years old. During the action-research four instruments were administered, one of which contains the research questions about the school chosen, post-diploma, family and self-image in 5 and 10 years whose results are reported in the article. They show that a percentage varying from 18% to 25% declares that they are going on studying. Only 25% in 5 years and 0% in 10 years sees themselves as a student, and this even if 46% thinks that their parents wish that they went on studying. On the other hand, a percentage varying from 33% to 52% declares that they are going to work straight away after high school and 40% in 5 years and 71% in 10 years sees themselves as a permanent employee, although only 33% thinks that their parents want that immediately. Doing both things, studying and working, is taken into account by a percentage varying from 5% to 11%. This percentage is reduced to 5% in 5 years and to 0% in 10. Finally, a percentage varying from 12% to 44% declares they do not know what they are going to do after high school
Burnout and Loss Aversion: how high-value losses (HVLs) on the job can expose workers to high strain - Part 1
No full textBurnout research has been applied to different aid fields, as well as to high stressful work sectors, and ultimately extended to most of the jobs. Still, in certain professions, the burnout issue is more relevant, especially when it comes to some subtended and unavoidable consequences, such as the loss experience. The stress caused by potential high-value losses (HVLs) in some jobs can be the primary source of suffering, detachment, cynicism, and strain at work. Moreover, decades of decision-making research, by using simple experimental tasks, has proved that a heterogeneity in loss aversion responses exists and is great. Individual differences may reflect a stable dispositional characteristic in avoiding at all costs consequences of HVLs stress, which could be implicated in burnout development. Drawing on the conceptualizations of burnout and research on loss aversion, we designed two within-subject studies using experimental tasks for measuring loss avoidance, in two different sectors, all associated by potential HVLs (i.e. medical, financial). The aim of this research is to explore how individual differences related to loss aversion are associated with burnout and its components. Based on such results we longitudinally model the causality between loss aversion and burnout
Motivazioni al volontariato e rischi psicosociali
No full textL’articolo riporta i risultati di una ricerca condotta con 348 soccorritori volontari in ambulanza di Croce Verde Verona sulle loro motivazioni a una scelta di volontariato che li espone a rischi psicosociali di distress e burnout. Tali rischi sono legati anche alle motivazioni (autocentrate vs eterocentrate, intrinseche vs estrinseche) che portano un individuo a fare questa scelta. Motivazioni estrinseche e autocentrate rendono la persona vulnerabile ai fattori distressogeni di fare il volontario in ambulanza, dal momento che tali rischi non sono adeguatamente rappresentati nella mente delle persone con tali motivazioni. Nell’ipotesi che differenti caratteristiche socio-anagrafiche si correlino a differenti motivazioni, la ricerca ha indagato eventuali differenze di motivazioni dichiarate tra diversi gruppi di volontari: maschi vs femmine, giovani vs anziani, single vs accoppiati. I risultati mostrano che le femmine risultano portatrici di motivazioni intrinseche eterocentrate, i maschi sono caratterizzati in particolare da motivazioni autocentrate, sia intrinseche che estrinseche, mentre i single sembrano essere portatori di motivazioni intrinseche autocentrate
Using a Multi-agent System to Simulate the Organizational Behaviour of Entrepreneurs and Managers
No full textA Multi-agent System to Simulate the Organizational Behaviour of Entrepreneurs and Manager
Biases, reasoning and personality in finance
Full text linkPoster presentato al 30th International Congress of Psychology (ICP), Cape Town, 22-27 July 2012 , 201
Can simulations perform decision-making and learning processes of teams?
No full textThis study aims to investigate which team factors have a significant influence on decision-making and on learning processes of teams. In order to understand which factors improve more, several relevant teams characteristics drawn from classical literature have been considered, such as: shared mental models (Anderson & West, 1998; Härtel, Härtel, & Barney, 1998), leadership (Scott & Bruce, 1994), size of groups (Shaw, 1981). In particular, we focused on Teamwork and Team Climate and dimensions related such as: Flexibility (Patterson et al., 2005), Vision, Participative safety, Support for innovation and Task orientation (West & Farr, 1990).
Considering team simulation research (business simulation, virtual financial contest, data-management games etc.) we explored a set of relationships among factors presented, which reflect authentic causal relations with the learning processes and the decisions of teams. We establish that three dimensions, which are: Flexibility, Communication and Support for Innovations have a crucial role in all the simulation studied. In specific, Flexibility delights the team reaction and its turn effects on the decision-making of teams. Basically, when a team is more “flexible”, members of the groups are more open to the innovations, participants feel supported by colleagues, they feel safe in the teamwork and the frequency of their interactions become higher. In addition, as result of these processes learning and decisions abilities improve.
The study illustrates that such dimensions play a key role in acquiring information and in learning aims, and those are essential characteristics in order to register a good performance in decisions, by sharing information and knowledge inside the group
Using an Agent-Based Model to Simulate Loss-aversion and Learning Behaviour among Investors
No full textPurpose: The aim of the present work is to design a model of stock market composed by virtual investors and to consider individual differences among these.
Design/Methodology: For this purpose we made use of an Agent-Based Model (ABM), where each agent in the simulation represent an individual investor capable to move in the virtual environment and to make transactions with other agents. Each agent was initially fitted with a virtual portfolio of investments and was generated comprising: (1) a personal risk tolerance, modelled within a loss-aversion perspective (2) time-life (3) an investment objective (4) a learning behaviour based on experience. The former and the latter features were based on a previous paper (Ceschi, Rubaltelli e Sartori, 2014) which implemented individual differences into the Value function (Kahneman, Tversky, 1979).
Results: Different scenarios were simulated changing the learning behaviour criteria and the number of investors within the simulation. All different scenarios generated are discussed in a comparison perspective.
Limitations: A limitation of the present study lies in the number of variables computed into the simulation model.
Research/Practical Implications: Nevertheless, the simulation is able to forecast different scenarios of stock markets considering individual differences (namely, the sensitivity to losses) among the investors.
Originality/Value: The present study is extremely valuable since it allows exploring many possible scenarios disclosing relevant consequences that can arise from psychological tendencies induced by positive and negative investment performance
The pursuit of happiness: A model of group formation
No full textWe developed an Agent-Based Model with the aim of investigating the effect of the interaction among several virtual actors characterized by (i) a certain level of emotional intelligence and (ii) an individual behavioral proneness to act positively or negatively within social interactions. The goal of each agent is to achieve a sat-isfactory internal state, which is consequential to the positive/negative effects de-rived by the incurred social interactions. As a result, when the simulation has been run, we have observed the spontaneous emergence of groups. Moreover, it could be easily noted that the large majority of the defectors are incapable to join to any group, and the few groups that accept defectors are not able to maintain more than one of this kind of actors. Finally, we studied the ratios between virtual actors when stable configurations are reached
- …
