Compassion and Prosocial Behavior. Is it Possible to Simulate them Virtually? Andrea Ceschi1, Andrea Scalco1, Stephan Dickert2,3, and Riccardo Sartori1 1
University of Verona, Department of Philosophy, Education and Psychology, Verona, Italy {andrea.ceschi,andrea.scalco,riccardo.sartori}@univr.it 2 WU Vienna University of Economics and Business 3 Linköping University, Linköping, Sweden
[email protected]
Abstract. In the field of artificial intelligence, a question dealing with computer and cognitive science is arising and becoming more and more crucial: Can we design agents so sophisticated that they are capable of mimicking emotional behaviors in general as well as specific emotions like compassion or empathy? Despite the production of different computational models, their integration with cognitive and psychological theories remains a central problem. Reasons are both methodological and theoretical. Primarily, it is difficult to quantify the impact of such factors as individual differences, inclinations and personality traits. In addition, Agent-Based Models (ABMs) often use linear dynamics, even in describing emotions, without considering the basis of psychophysics. Bearing in mind this and focusing on compassion as a particular emotion, the paper aims to present a “Decalogue” for those interested in designing agents capable of mimicking human emotional behaviors. In the paper, compassion will be translated as prosocial behavior. Keywords: Emotional behaviors, Compassion, Prosocial Behavior, Psychophysics.
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Advances and Limits in Computing and Reproducing Emotional Behaviors
Emotions are considered important for computational models because they can provide both an explanation for agents’ adaptive behaviors (Anderson et al., 2004; Scheutz & Sloman, 2001) and ideas to facilitate social cooperation in real life (Gratch & Marcella, 2001; Keltner & Haidt, 1999). Gratch and Marsella (2004) consider the development of computational models of emotions as a core research focus that will facilitate both the interpretation of and the influence on human behavior. Emotion is essential for thought as much as thought is essential for emotion (Lazarus, 1982). That is why it is important to consider them together. Lin, Spraragen, Blythe, and Zyda (2011) describe three main computational models that show different aspects of the relationship between emotion and cognition: © Springer International Publishing Switzerland 2015 J. Bajo et al. (eds.), Trends in Prac. Appl. of Agents, Multi-Agent Sys. and Sustainability, Advances in Intelligent Systems and Computing 372, DOI: 10.1007/978-3-319-19629-9_23
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1. EMA (Marsella & Gratch, 2009): Mostly interested in explaining the dynamics of emotions through a sequence of events, it focuses on appraisal and how this leads first to emotions and then to coping. The model shows how building notconfirmed expectations could lead to very specific emotions (delusion, frustration, etc.). 2. Soar-Emote (Marinier III, Laird, & Lewis, 2009): It shows how different appraisals influence the notion of intensity. In particular, authors propose to use appraisal to calculate both arousal and valence in various models. 3. WASABI (Becker-Asano & Ishiguro, 2009): It is supposed to be one of the most general models of emotions built to simulate affective agents. The model uses primary and secondary emotions. The first ones are more instantaneous while the second ones are more complex and arise from reasoning. Since then, much progress has been made both in the field of computer science and in the study of emotions. Despite the production of different computational models, their integration with solid psychological and cognitive theories remains a central problem . Reasons are both methodological and theoretical. Primarily, the lack of interdisciplinarity makes it difficult to adapt theories to classical models based on agents without simplifying the factors present in them. Secondly, it is difficult to quantify the impact of such factors as individual differences, inclinations and personality traits (Sartori, 2010; Sartori & Pasini, 2007). Thirdly, Agent-Based Models (ABMs) often use linear dynamics, even in emotions, without considering the basis of psychophysics. Despite the limitations presented so far, it is important to state that ABMs represent a true revolution for psychology and cognitive science, probably the first instrument able to dynamically reproduce or mimic emotions connected to behaviors. All this considered, the paper aims to present a “Decalogue” for those interested in designing agents capable of mimicking human emotional behaviors. In particular, the present contribution will try to give some useful suggestions in order to answer the three limits mentioned above by presenting a case study related to compassion and prosocial behavior.
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Using Cognitive and Psychological Studies for Better Simulations
In order to design agents with appropriate characteristics, interdisciplinary methods are crucial. In particular, it is worth considering those theories developed by psychology and cognitive science that outline perspectives that should be considered in designing agents. In the specific case of compassion and prosocial behavior, social scientists and cognitivists have developed several theories to explain them. Batson and Oleson (1991), for example, believe that the main goal of prosocial behavior is to make others feel better, and, in this regard, empathy assumes a leading role. Empathy is a social-affective dimension that can be developed by training experiences and is at the basis of interaction and relationships (Cunico et al., 2012). Other theories are more “egoistic” than this and suggest that, when deciding to give help, our benefits are more important than others’
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benefits. Andreoni (1989) states that, when we help someone, we experience a sort of “warm-glow” feeling inside and this would be the main motivation for altruism. On the other hand, Cialdini et al. (1987) think that we give help particularly when the suffering of others leads to negative feelings that we want to alleviate. Most scientists think that once we are able to identify the main motivators we can understand and predict prosocial behavior more easily. However, the question is whether experimental studies are able to respond to a huge number of interactions of different types while they are happening between people who give help and people who need help. What happens, for example, if, after you have just helped someone, you meet a person that you cannot help or even two people in need, one after the other, which cannot be helped? How does compassion change over time in a system where there are people who cannot be helped? For social psychologists and cognitive scientists, these questions appear to be at the same time extremely interesting and difficult to answer by simply relying on classical experimental approach. For this reason, the use of ABMs is becoming precious, especially if these models are computed by starting from empirical theories.
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Better to Start from SEMs. The Importance of the Empirical Data
It is well known that the development of ABM algorithms is the most fragile aspect of the simulation analysis. In the specific case of ours, in order to design social networks, the crucial preference is the identification of an amount of agent’s attributes significant for prosocial behavior. The theories we have dealt with before are able to help. Agents’ attributes span from basic demographic attributes to more specific and psychological features (Ceschi, Sartori, & Rubaltelli, 2014). These attributes have the greatest impact and therefore it is important to consider them for the aims of the analysis. For this reason, it is recommended to start from some empirical models, such as Structural Equation Models (SEMs), which are widespread in social and psychological science and they are used to represent human behaviors. They consist of equations that link latent variables and their indicators by using exogenous causes. A remarkable benefit of this framework is that correlations of observed indicators are clearly made as arising out of subjacent factors that are accountable for the results (Krishnakumar & Ballon, 2008). Apart from this, results from SEMs have never been dynamically linked to individual differences. This is a limit of this quantitative method. Essentially, individual differences are characterized as a set that makes individuals particular, according to their inclinations, capabilities and outcomes. Starting from here, ABMs can help to represent, in a natural and dynamic way, several scales of analysis and the importance of structures at different levels, none of which is easy to accomplish with other modeling techniques (Gilbert & Terna, 2000). In the current paper, the aim of the analysis is to present a model able to simulate a number of characteristics that have been scaled, modeled and assigned with probability distributions to simulate prosocial behavior. Usually, the purpose of this stochastic effort is to endow agents with a ‘personality’. For this reason, SEMs on prosocial behaviors (Maner & Gailliot, 2007; Ranganathan & Henley, 2008) have been considered as determinant factors of prosocial behavior itself.
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How to Compute Compassion over Time. A Solution from Psychophysics
Commonly, the good feeling you get when you help someone in need drops over time following the psychophysical function of human sensitivity (Weber, 1834). While this sounds normal in psychophysical terms (imagine the difference in sensation when you turn on the light in a dark room and when you do the same thing in an already lighted space), how normal does that sound when we evaluate somebody’s life differently just because they are the first or the nth needing help? On the other hand, the greater the number of people you cannot help, the lower is the good feeling of you experience about those you can help. Fetherstonhaugh, Slovic, Johnson and Friedrich (1997) found that people have a diminishing sensitivity to the value of life over time, which they called Psychophysical Numbing (Figure 1). Västfjäll and Slovic (2011) found that participants reported a decrease in feeling good about giving help during the increase of the number of people that could not be helped. The phenomenon has been called the Pseudo-Inefficacy effect (Västfjäll & Slovic, 2011) and it is a further example that people not always respect the utilitarianism norms (Dickert, Västfjäll, Kleber, & Slovic (2012).
Fig. 1. Trend of Psychophysical Numbing
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A Proposal for a Realistic Simulation of Compassion and Prosocial Behavior
Naturally, factors that motivate helping and factors that demotivate helping are connected. It is therefore fundamental to study them in their interactions with altruistic behavior. Designing agents capable of reproducing compassion and prosocial behavior requires different algorithms that must be integrated so that they can work together and take into considerations the points and theories previously seen. In order to create a model capable of recreating compassion, it is important to consider that “compassionate agents” will be influenced by both agents that can be helped and cannot be helped. “Compassionate agents” should be demotivated if they meet
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agents who cannot be helped. On the other hand, they should be motivated after helping others. The model should include 3 variables which play an important role in prosocial behavior: 1. Compassion (C), 2. Demotivation (D) and 3. Altruism (A). Let’s suppose that A is the need to help that individuals feel in certain situations and that they will be repaid with C. This mechanism happens when agents can help other agents. If the helper cannot find someone to help, functions invert their trends (C starts decreasing, while A starts increasing). D decrements A and C levels when the agent meets others that cannot be helped. In order to compute mathematically these trends (Figure 2), we used several logarithmic and decay functions based on coefficients extracted from SEMs on prosocial behaviors (Maner & Gailliot, 2007; Ranganathan & Henley, 2008).
Fig. 2. Trends of Compassion, Demotivation and Altruism, where X is the standardized level of C, D and A or of the feeling observed, and Y the time
In phase I, an agent has the possibility to help agents in all the time units. Consequently, its level of A increases and its level of C decreases. In phase II, the helper starts meeting agents that cannot be helped. Its levels of C and A go down while D increases. In phase III, the helper finds agents to help, and, therefore, D stops amounting and starts decreasing. C increases, A decreases. In phase IV, the helper is no more able to find other agents. Consequently, C decreases and A increases, while D continues to decrease. Next section will suggest a mathematical model for the example here reported (Table 1). Table 1. Definitions and computation of the variables of the present example Name t C D A C0 m λ μ
Meaning the time variable the Compassion level the Demotivation level the Altruism level the maximum value of Compassion the states of agents are: a. when the agent does not meet others; b. when the agent meets an agent in need of help; c. when the agent meets an agent in need of help, but is not able to help it. the constant value of the Demotivation factor which influences the Compassion the constant value of the Demotivation factor
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The compassion function C function of phase I
t0 = ln(C0 / (C0 - Ct2 ))
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C function of phase ⎧⎪ C0 e -t C(t) = ⎨ -t ⎪⎩ C0 (1 - e )
Equations are used to compute the t1 and t0 respectively.
⎧ C0 e-(t -(t3 -t0 )) |m= a ⎪⎪ -(t -(t2 -t1 )) C(t)= ⎨C0 (1- e ) |m= b ⎪ t -t1 -t ⎪⎩ 1 − λ C0e | m = c
C function of phase II
The demotivation function
⎧⎪C0 e-(t -(t3 -t0 )) |m= a C(t)= ⎨ -(t -(t2 -t1 )) ) |m= b ⎪⎩C0 (1 - e
⎧ 0_____ _ ____ | m = a ⎪ D(t ) = ⎨C0 e-(t -(t2 -t1 )) _ _ _ | m = b ⎪ -(t -tIII ) ) |m= c ⎩ C0 (1 - e
Where t is the current time. t0 is the actual time when Equation reaches the value C(t3) t1 is the actual time when Equation reaches the value C(t2). t2 is the moment when the agent does not find people to help. t3 is the moment the agent find people he can help.
The altruism function
t2 is the moment when the agent does not find people to help.
⎧C0 (1 - e-(t -(t3 -t0 )) ) |m= a ⎪⎪ -(t -(t2 -t1 )) A(t)= ⎨ C0 (1 - e ) |m= b ⎪ -(t -(t3 -t0 )) -(t -(t3 -t0 )) ) - C0 e |m= c ⎪⎩ C0 (1 - e
C function of phase III t1 = ln(C0 / Ct3 )
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Where t is the latest moment when the agent does not find people to help.
Conclusion
As Gratch and Marsella (2004) claim, including elements related to emotions in ABMs can facilitate both the interpretation of and the influence on human behavior. In this model, we used compassion as a predictor of the helping behavior, following a method that takes into account solid psychological theories and experimental data. The model, here presented just for the mathematical part, is finding application in a simulation that aims to recreate agents capable of reproducing compassion and the feeling of inefficacy that can be found often in charity giving issues, where the help needed is always bigger than the material resources that others can offer. As well, by using these algorithms, we aim to simulate contexts to manage natural disasters, epidemic situations, medical problems and several other events where helping behavior is crucial.
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As for this basic model, we only included a simple relation between agents in need. On the other hand, and quoting the famous saying by Korzybski, the map is not the territory. In real life, there can be many other factors that would be worth being implemented in this relation. Demotivation and motivation to help can be mediated or moderated by other factors such as mood, previous experiences (not everyone feels the same demotivation when they learn about individuals they cannot help), coping strategies, etc. On the other side, the actual help might be also influenced by the characteristics of the individuals in need, such as gender, race, physical conditions and others. Several other characteristics could be implemented into the mathematical model in order to run even more realistic simulations regarding prosocial behavior.
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