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Video – TED lecture: Empathy, cooperation, fairness and reciprocity – caring about the well-being of others seems like a very human trait. But Frans de Waal shares some surprising videos of behavioural tests, on primates and other mammals, that show how many of these moral traits all of us share. (TED, Nov. 2011, link).

Evolutionary explanations are built around the principle that all that natural selection can work with are the effects of behaviour – not the motivation behind it. This means there is only one logical starting point for evolutionary accounts, as explained by Trivers (2002, p. 6): “You begin with the effect of behaviour on actors and recipients; you deal with the problem of internal motivation, which is a secondary problem, afterwards. . . . [I]f you start with motivation, you have given up the evolutionary analysis at the outset.” ~ Frans B.M. de Waal, 2008.

Do animals have morals? And above all, did morality evolved? The question is pertinent in a broad range of quite different areas, as in as well Computer Sciences and Norm Generation (e.g. link for an MSc thesis) in bio-inspired Computation and Artificial Life, but here new fresh answers come directly from Biology. Besides the striking video lecture above, what follows are 2 different excerpts (abstract and conclusions) from a 2008 paper by Frans B.M. de Waal (Living Links Center lab., Emory University, link): de Waal, F.B.M. (2008). Putting the altruism back in altruism: The evolution of empathy. Ann. Rev. Psychol. 59: 279-300 (full PDF link):

(…) Abstract: Evolutionary theory postulates that altruistic behaviour evolved for the return-benefits it bears the performer. For return-benefits to play a motivational role, however, they need to be experienced by the organism. Motivational analyses should restrict themselves, therefore, to the altruistic impulse and its knowable consequences. Empathy is an ideal candidate mechanism to underlie so-called directed altruism, i.e., altruism in response to another’s pain, need, or distress. Evidence is accumulating that this mechanism is phylogenetically ancient, probably as old as mammals and birds. Perception of the emotional state of another automatically activates shared representations causing a matching emotional state in the observer.With increasing cognition, state-matching evolved into more complex forms, including concern for the other and perspective-taking. Empathy-induced altruism derives its strength from the emotional stake it offers the self in the other’s welfare. The dynamics of the empathy mechanism agree with predictions from kin selection and reciprocal altruism theory. (…)

(…) Conclusion: More than three decades ago, biologists deliberately removed the altruism from altruism.There is now increasing evidence that the brain is hardwired for social connection, and that the same empathy mechanism proposed to underlie human altruism (Batson 1991) may underlie the directed altruism of other animals. Empathy could well provide the main motivation making individuals who have exchanged benefits in the past to continue doing so in the future. Instead of assuming learned expectations or calculations about future benefits, this approach emphasizes a spontaneous altruistic impulse and a mediating role of the emotions. It is summarized in the five conclusions below: 1. An evolutionarily parsimonious account (cf. de Waal 1999) of directed altruism assumes similar motivational processes in humans and other animals. 2. Empathy, broadly defined, is a phylogenetically ancient capacity. 3. Without the emotional engagement brought about by empathy, it is unclear what could motivate the extremely costly helping behavior occasionally observed in social animals. 4. Consistent with kin selection and reciprocal altruism theory, empathy favours familiar individuals and previous cooperators, and is biased against previous defectors. 5. Combined with perspective-taking abilities, empathy’s motivational autonomy opens the door to intentionally altruistic altruism in a few large-brained species.(…) in, de Waal, F.B.M. (2008). Putting the altruism back in altruism: The evolution of empathy. Ann. Rev. Psychol. 59: 279-300 (full PDF link).

Frans de Waal research work does not end up here, of course. He is a ubiquitous influence and writer on many related areas such as: Cognition, Communication, Crowding/Conflict Resolution, Empathy and Altruism, Social Learning and Culture, Sharing and Cooperation and last but not least, Behavioural Economics. All of his papers are free on-line, in a web page I do vividly recommend a long visit.

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[…] Nash: See if I derive an equilibrium (link) where prevalence is a non-singular event where nobody loses, can you imagine the effect that would have on conflict scenarios, arm negotiations… (…) currency exchange? […], in Memorable quotes for “A Beautiful Mind” (2001), movie directed by Ron Howard, starring Russell Crowe, along with Ed Harris.

” […] What I refused to see is what the prisoner’s dilemma teaches: anyone who plays the “All Cooperate” strategy is a sucker, and incents the other to defect on every move. I now believe that the lesson of the prisoner’s dilemma is that a robust ethic succeeds where a weak one fails. Be fair, be strong, reward cooperation and punish defection, and you will have nothing to regret. […] “, in An Ethic Based on the Prisoner’s Dilemma, The Ethical Spectacle, September, 1995.

[…] Martin Nowak is known for his many influential papers on cooperation and in theoretical biology. This book is a popular writing on his scientific adventures, personal motivations and collaborations. Given his work it is remarkable is that this book does contain nor mathematical equations neither graphical illustrations. Nowak is currently a professor of mathematics and biology at Harvard University. Moreover, he directs since 2003 his own research program on Evolutionary Dynamics. This program has been made possible by a 30 million pledge by Wall Street tycoon Jeffrey Epstein. This is just one ingredient of the remarkable story of Nowak scientific life. The book starts with laying out the puzzle of cooperation illustrated by the prisoner’s dilemma. If both players are selfish and rational they will defect. Why do we see so much cooperation in human societies and other domains of the biological world? This puzzle was introduced to Nowak by Karl Sigmund, a professor in mathematics from the University of Vienna, while Nowak was a student in biochemistry. Sigmund talked about the famous Axelrod tournament and Nowak got hooked. The tournament of Axelrod assumed that the strategies did not make errors. What if there are errors? Will Tit for Tat still be a good strategy? His analysis showed that a more promising strategy is a more Win Stay, Loose Shift. This strategy leads to cooperation if both agents do the same, and defect if not. Hence agents can forgive.

The analysis of strategies that do well in direct reciprocity is one of the five chapters in which Nowak discuss five ways in which the prisoner’s dilemma can be solved. The second chapter is on indirect reciprocity. In a landmark paper with Karl Sigmund Nowak showed that when agents derive information on their reputation (image score) cooperation can evolve in one-shot prisoner’s dilemma. The third chapter is on spatial games and features another landmark paper on spatial chaos. This paper, written with Lord Robert May, shows that cooperation can evolve if agents interact with neighbours and imitate the best strategy of their neighbours. The forth chapter is on group selection. This controversial approach is now better known as multi-level selection. Finally, the fifth chapter is on kin-selection, the first theory on cooperation based on genetic relatedness. The discussion on the five ways to overcome the prisoner’s dilemma is especially interesting due to the discussion on the scientific process. How long hikes with Sigmund let to inspirations that let Nowak drop all other activities he was working on. How chance meetings let to new ideas. How he got, to Oxford, Princeton and finally Harvard.

In the second part of the book discusses cooperation in biology. It covers his applications to the origins of life, the study of cancer and the dominance of ant colonies. This work might be less familiar to the readers of JASSS. Especially the work on cancer, defectors in our own biology, can lead to practical applications. The final part of the book focuses on human societies. Humans are called supercooperators since they are the only organism that uses all five ways to solve social dilemmas. First the evolution of language is discussed. Nowak made important contributions to the study of language by simulating agents benefiting from mutual understanding in language games. According to Nowak, the emergence of language is the most important development in life since 600 million years. It resulted to new types of cooperation. Especially in the context of indirect reciprocity it is key to have language. We need gossip and other types of information transmission to derive reliable estimates on the reputation of strangers.

Then Nowak discusses public goods and the use of costly punishment to derive cooperation. This is the only part of the book where he discusses empirical research. With two graduate students he performed experiments which showed that punishment is not something special, but in line with earlier work on reciprocity and tit for tat. Then Nowak continues with his recent work on network theory and set theory. The book closes with a reflection on the consequences of his work. Cooperation is a crucial ingredient to evolution, but there always will be cycles. The question is how to re-establish cooperation after it has been collapsed. This book provides a nice overview of the findings of Nowak’s work. Note however, that Nowak has substantial work in other areas of research not discussed in the book such as infectious diseases. Together with science writer Roger Highfield, Nowak provides an inspirational story on science in practice. This covers the importance of his mentors in his early years, and his current role of a mentor to his students at Harvard. In conclusion, this is a marvellous book. Although I may not always agree with the findings of Nowak’s research, it is a motivating account on the messy practice of science. I highly recommend this book for students and faculty in social simulation and science in general. […], Reviewed by Marco A. Janssen
(Arizona State University) on JASSS 2011 [Nowak, Martin, Supercooperators: Altruism, Evolution, and Why We Need Each Other to Succeed, ISBN 9781439100189 (pb), Free Press (The): New York, NY, 2011].

“I was never interested in Facebook or MySpace because they feel like malls to me. Twitter actually feels like the street. You can bump into anybody on Twitter.” — Science-fiction novelist William Gibson – New York (October 11, 2010)

“There is almost certainly an evolutionary drive toward increasing complexity in the face of entropy. That’s practically a definition of life. Technology is so powerful and attractive to us because it holds the promise of greater complexity and greater connectedness. Atoms to molecules to cells to organelles to organisms. What’s next? No one knows for sure, but it sure ain’t Facebook.” — American media theorist Douglas Rushkoff, writer, columnist, early cyberpunk culture adopter, and advocacy of open source solutions to social problems.

Precursors of social networks in the late 1800s include Émile Durkheim and Ferdinand Tönnies. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (gemeinschaft) or impersonal, formal, and instrumental social links (gesellschaft) [in Linton Freeman, “The Development of Social Network Analysis“, Vancouver, Empirical Press, 2004 (here is a valuable must-read review on it)]. Durkheim gave a non-individualistic explanation of social facts arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors. He distinguished between a traditional society – “mechanical solidarity” – which prevails if individual differences are minimized, and the modern society – “organic solidarity” – that develops out of cooperation between differentiated individuals with independent roles. Then, Georg Simmel (1908-1971), writing at the turn of the twentieth century, was the first scholar to think directly in social network terms. His essays pointed to the nature of network size on interaction and to the likelihood of interaction in ramified, loosely-knit networks rather than groups.

Nowadays, however, the paraphernalia of increasing intelligent tools (network metrics) are widely available, mainly to the exponential role of the science of complex networks. As stated by Matthias Scholz (Network Science.org / Webpage)  (…) Network science has received a major boost caused by the widespread availability of huge network data resources in the last years. One of the most surprising findings, popularized by Albert-László Barabási and his team, is that real networks behave very distinct from traditional assumptions of network theory. Traditionally, real networks were supposed to have a majority of nodes of about the same number of connections around an average. This is typically modelled by random graphs. However, modern network research revealed that the majority of nodes of real networks is very low connected, and, by contrast, there exists some nodes of very extreme connectivity (hubs). This power-law characteristics, termed scale-free by Barabási, can be found in many complex real networks from biological (natural) to social man-made networks (…).

Book cover – Linton Freeman, “The Development of Social Network Analysis“, Vancouver, Empirical Press, 2004 (and a valuable review on it).

While embedding themselves on social-networking, people do tend to forget this, of course, but here are 2 or 3 things you should know about Social Networks before stupefying registering yourself on FarmVille (actually, this is a limited list of some of the actual metrics usually employed on current network analysis provided with a short description – So, … do really ponder yourself where you are – on the street or in the mall!):

Betweenness (link) The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the node’s neighbours, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links. | Bridge (link) An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph. | Centrality (link) This measure gives a rough indication of the social power of a node based on how well they “connect” the network. “Betweenness”, “Closeness”, and “Degree” are all measures of centrality. | Centralization (link) The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses. | Closeness (link) The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the “grapevine” of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network.  The shortest path may also be known as the “geodesic distance”. | Clustering coefficient (link) A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater ‘cliquishness’. | Cohesion (link) The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted. | …

Fig. – Hue (from red=0 to blue=max) shows the node betweenness. (link)

… Degree (link) The count of the number of ties to other actors in the network. See also degree (graph theory). | (Individual-level) Density (link) The degree a respondent’s ties know one another/ proportion of ties among an individual’s nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks). | Flow betweenness centrality (link) The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node). | Eigenvector centrality (link) A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question. | Local Bridge (link) An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle. | Path Length (link) The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes. | Prestige (link) In a directed graph prestige is the term used to describe a node’s centrality. “Degree Prestige”, “Proximity Prestige”, and “Status Prestige” are all measures of Prestige. See also degree (graph theory). | Radiality (link) Degree an individual’s network reaches out into the network and provides novel information and influence. | Reach (link) The degree any member of a network can reach other members of the network. | Structural cohesion (link) The minimum number of members who, if removed from a group, would disconnect the group. | Structural equivalence (link) Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent. | Structural hole (link)  Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.

Of course, some of these metrics are redundant over each other, and in fact there is some intelligence on having this on-purpose redundancy. But I would say that Path Length and Cliqueness, Eigenvector centrality, Betweenness, Clustering coefficient, Degree and last but not least Flow (btw, here‘s my own poetic lateral view of it), would be the most important of them all, even if, these all depends on what you are, on what you see, on what you feel, and mostly on what you somehow rather expect from it as a whole experience over time. So, finally, let me just add that all these metrics will not avoid people from writing/sharing experiences like: Son las 7 y 30 y estoy cagando” followed by “Son las 5 y 31 y ya cagado“. Unfortunately, what follows is precisely a video parody on Facebook not far from his own reality. As usual, you are always free to choose from the geriatric-like shelter malls or the open-air streets…

Fig. – Christ having some problems on passing the right message. Comic strip from Zach Weiner (Saturday Morning Breakfast Cereal blog – smbc-comics.com ).

Social psychologists, sociologists, and economists have all proposed theories of norm emergence. In general, they views norm emergence as depending on three factors: (i) actors’ preferences regarding their own behaviour (inclinations); (ii) actors’ preferences regarding the behaviour of others (regulatory interests); and (iii) measures for enforcing norms (enforcement resources), such as access to sanctions and information. Whereas most studies of norm emergence have focused on inclinations or enforcement resources, this article analyses the role of regulatory interests in norm emergence. Specifically, it analyses systems of collective sanctions in which, when and individual violates or complies with a rule, not merely the individual but other members of that person’s group as well are collectively punished of rewarded by an external agent. These collective sanctions give individuals an incentive to regulate one another’s behaviour. This paper demonstrates that when a group is subjected to collective sanctions, a variety of responses may be rational: the group may either create a secondary sanctioning system to enforce the agent’s dictates, or it may revolt against the agent to destroy its sanctioning capacity. According to the proposed theoretic model. the optimal response depends quite sensitively on the group’s size, internal cohesion, and related factors. Abstract: D.D. Heckathorn, “Collective sanctions and the creation of prisoner’s dilemma norms“, American Journal of Sociology (1988), Volume: 94, Issue: 3, Publisher: University of Chicago Press, Pages: 535-562.

Video – […] see, in this world, there are two kinds of people, … my friend, … those with ‘loaded guns’ and those who dig. You dig. […] Last 8 minutes finale of The Good, the Bad and the Ugly (Il buono, il brutto, il cattivo), a 1966 Italian epic spaghetti western film directed by Sergio Leone, starring Lee Van Cleef, Eli Wallach and Clint Eastwood in the title roles, playing a kind of 3-agent Prisoner’s dilemma game. Now, one of them, the Good (Clint Eastwood) is the only who knows he is in fact just playing a 2-agent PD game. And that,  besides the inner non-linearity complexity of the ‘game’, makes all the difference…

[...] People should learn how to play Lego with their minds. Concepts are building bricks [...] V. Ramos, 2002.

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