Why Trump cooperates with Putin

Some thoughts about Trump’s press conference with Putin, as opposed to the G7 and NATO meetings, from a game-theoretic perspective. Yes, it’s time for some (more) game theory!

Consider the iterated prisoner’s dilemma (IPD), where two prisoners are being interrogated by the police. They have two choices: COOPERATE by remaining silent, or DEFECT by confessing. If both remain silent, they get a light punishment, since the police can’t prove anything. If one prisoner confesses while the other remains silent, the confessing prisoner goes free and the other faces the steepest punishment. If they both confess, they get a moderate punishment.

Axelrod, in The Evolution of Cooperation, shows that there are several strategies that one can use in the IPD and that these strategies vary by the amount of contact expected in the future. If none or very little future interaction is expected, then it pays to DEFECT, which basically means to screw your opponent.

If, on the other hand, there is an expectation of extensive future contact, the best strategy is some form of TIT-FOR-TAT, which means that you start by cooperating with your opponent, but if they defect, then you match that defection with their own. If they cooperate, then you match that as well.

This turns out to be a simple, clear strategy that rewards cooperative behavior and punishes jerks. It is powerful enough that a small cluster of TIT-FOR-TAT can invade a population of ALL_DEFECT. It has some weaknesses as well. We’ll get to that later.

Donald Trump, in the vast majority of his interactions has presented an ALL_DEFECT strategy. That actually can make sense in the world of real-estate, where there are lots of players that perform similar roles and bankruptcy protections exist. In other words, he could screw his banks, partners and contractors and get away with it, because there was always someone new.

But with Russia in general and Putin in particular, Trump is very cooperative. Why is this case different?

It turns out that  after four bankruptcies (1991, 1992, 2004 and 2009) it became impossible for Trump to get loans through traditional channels. In essence, he had defected on enough banks that the well was poisoned.

As the ability to get loans decreased, the amount of cash sales to Russian oligarchs increased. About $109 million were spent purchasing Trump-branded properties from 2003 – 2017, according to MccLatchy. Remember that TIT-FOR-TAT can win over ALL_DEFECT if there is prolonged interaction. Fourteen years is a long time to train someone.

Though TIT-FOR-TAT is effective, it’s hard work trying to figure out what the other player is likely to do. TIT-FOR-TAT’s weakness is its difficulty. We simply can’t do Nash equilibria in our heads. However, there are two cognitively easy strategies in the IPD: ALL_DEFECT, and ALL_COOPERATE. Trump doesn’t like to work hard, and he doesn’t listen to staff, so I think that once Trump tried DEFECT a few times and got punished for it he went for ALL_COOPERATE with the Russians. My guess is that they have a whole team of people working on how to keep him there. They do the work so he doesn’t have to think about it.

Which is why, at every turn, Trump cooperates. He knows what will happen if he doesn’t, and frankly, it’s less work than any of the other alternatives. And if you really only care for yourself, that’s a perfectly reasonable place to be.

Postscript

I’ve had some discussions about this where folks are saying “That’s too much analysis for this guy. He’s just an idiot who likes strongmen”. But here’s the thing. It’s not about Trump. It’s about Putin.

What do you think the odds were on Trump winning the election in 2015? Now how about 2003, when he started getting Russian cash to prop up his businesses? For $110M, or the price of ONE equipped F/A-18, amortized over 14 years, they were able to secure the near total cooperation of a low-likelihood presidential contender/disruptor and surprise winner.

This is a technique that the Russians have developed and refined for years. So you have to start asking the questions about other individuals and groups that are oddly aligned with Putin’s aims. Russia has a budget that could support thousands of “investments” like Trump, here and abroad.

That’s the key. And that’s my bet on why Mueller is so focused on finances. The Russians learned an important lesson in spending on weapons in the Reagan administration. They can’t compete on the level of spending. So it appears that they might be allocating resources towards low-cost social weaponry to augment their physical capabilities. If you want more on this, read Gerasimov’s The value of Science is in the Foresight.

From I to We: Group Formation and Linguistic Adaption in an Online Xenophobic Forum

From I to We: Group Formation and Linguistic Adaption in an Online Xenophobic Forum

Authors

Venue: Journal of Social and Political Psychology

Quick takeaway:

  • Linguistic study of a xenophobic online chat room using Pennebaker’s LIWC text analytic system. Users who stay in the group change from individual to group pronouns and align linguistically. Cognitive complexity also appears to reduce as users align with the group

Abstract:

  • Much of identity formation processes nowadays takes place online, indicating that intergroup differentiation may be found in online communities. This paper focuses on identity formation processes in an open online xenophobic, anti-immigrant, discussion forum. Open discussion forums provide an excellent opportunity to investigate open interactions that may reveal how identity is formed and how individual users are influenced by other users. Using computational text analysis and Linguistic Inquiry Word Count (LIWC), our results show that new users change from an individual identification to a group identification over time as indicated by a decrease in the use of “I” and increase in the use of “we”. The analyses also show increased use of “they” indicating intergroup differentiation. Moreover, the linguistic style of new users became more similar to that of the overall forum over time. Further, the emotional content decreased over time. The results indicate that new users on a forum create a collective identity with the other users and adapt to them linguistically.

Notes:

  • Social influence is broadly defined as any change – emotional, behavioral, or attitudinal – that has its roots in others’ real or imagined presence (Allport, 1954). (pg 77)
  • Regardless of why an individual displays an observable behavioral change that is in line with group norms, social identification with a group is the basis for the change. (pg 77)
  • In social psychological terms, a group is defined as more than two people that share certain goals (Cartwright & Zander, 1968). (pg 77)
  • Processes of social identification, intergroup differentiation and social influence have to date not been studied in online forums. The aim of the present research is to fill this gap and provide information on how such processes can be studied through language used on the forum. (pg 78)
  • The popularity of social networking sites has increased immensely during the last decade. At the same time, offline socializing has shown a decline (Duggan & Smith, 2013). Now, much of the socializing actually takes place online (Ganda, 2014). In order to be part of an online community, the individual must socialize with other users. Through such socializing, individuals create self-representations (Enli & Thumim, 2012). Hence, the processes of identity formation, may to a large extent take place on the Internet in various online forums. (pg 78)
  • For instance, linguistic analyses of American Nazis have shown that use of third person plural pronouns (they, them, their) is the single best predictor of extreme attitudes (Pennebaker & Chung, 2008). (pg 79)
  • Because language can be seen as behavior (Fiedler, 2008), it may be possible to study processes of social influence through linguistic analysis. Thus, our second hypothesis is that the linguistic style of new users will become increasingly similar to the linguistic style of the overall forum over time (H2). (pg 79)
  • This indicates that the content of the posts in an online forum may also change over time as arguments become more fine-tuned and input from both supporting and contradicting members are integrated into an individual’s own beliefs. This is likely to result (linguistically) in an increase in indicators of cognitive complexity. Hence, we hypothesize that the content of the posts will change over time, such that indicators of complex thinking will increase (H3a). (pg 80)
    • I’m not sure what to think about this. I expect from dimension reduction, that as the group becomes more aligned, the overall complex thinking will reduce, and the outliers will leave, at least in the extreme of a stampede condition.
  • This result indicates that after having expressed negativity in the forum, the need for such expressions should decrease. Hence, we expect that the content of the posts will change such that indicators of negative emotions will decrease, over time (H3b). (pg 80)
  • the forum is presented as a “very liberal forum”, where people are able to express their opinions, whatever they may be. This “extreme liberal” idea implies that there is very little censorship the forum is presented as a “very liberal forum”, where people are able to express their opinions, whatever they may be. This “extreme liberal” idea implies that there is very little censorship, which has resulted in that the forum is highly xenophobic. Nonetheless, due to its liberal self-presentation, the xenophobic discussions are not unchallenged. For example, also anti-racist people join this forum in order to challenge individuals with xenophobic attitudes. This means that the forum is not likely to function as a pure echo chamber, because contradicting arguments must be met with own arguments. Hence, individuals will learn from more experienced users how to counter contradicting arguments in a convincing way. Hence, they are likely to incorporate new knowledge, embrace input and contribute to evolving ideas and arguments. (pg 81)
    • Open debate can lead to the highest level of polarization (M&D)
    • There isn’t diverse opinion. The conversation is polarized, with opponents pushing towards the opposite pole. The question I’d like to see answered is has extremism increased in the forum?
  • Natural language analyses of anonymous social media forums also circumvent social desirability biases that may be present in traditional self-rating research, which is a particular important concern in relation to issues related to outgroups (Maass, Salvi, Arcuri, & Semin, 1989; von Hippel, Sekaquaptewa, & Vargas, 1997, 2008). The to-be analyzed media uses “aliases”, yielding anonymity of the users and at the same time allow us to track individuals over time and analyze changes in communication patterns. (pg 81)
    • After seeing “Ready Player One”, I also wonder if the aliases themselves could be looked at using an embedding space built from the terms used by the users? Then you get distance measurements, t-sne projections, etc.
  • Linguistic Inquiry Word Count (LIWC; Pennebaker et al., 2007; Chung & Pennebaker, 2007; Pennebaker, 2011b; Pennebaker, Francis, & Booth, 2001) is a computerized text analysis program that computes a LIWC score, i.e., the percentage of various language categories relative to the number of total words (see also www.liwc.net). (pg 81)
    • LIWC2015 ($90) is the gold standard in computerized text analysis. Learn how the words we use in everyday language reveal our thoughts, feelings, personality, and motivations. Based on years of scientific research, LIWC2015 is more accurate, easier to use, and provides a broader range of social and psychological insights compared to earlier LIWC versions
  • Figure 1c shows words overrepresented in later posts, i.e. words where the usage of the words correlates positively with how long the users has been active on the forum. The words here typically lack emotional content and are indicators of higher complexity in language. Again, this analysis provides preliminary support for the idea that time on the forum is related to more complex thinking, and less emotionality.
    • WordCloud
  • The second hypothesis was that the linguistic style of new users would become increasingly similar to other users on the forum over time. This hypothesis is evaluated by first z-transforming each LIWC score, so that each has a mean value of zero and a standard deviation of one. Then we measure how each post differs from the standardized values by summing the absolute z-values over all 62 LIWC categories from 2007. Thus, low values on these deviation scores indicate that posts are more prototypical, or highly similar, to what other users write. These deviation scores are analyzed in the same way as for Hypothesis 1 (i.e., by correlating each user score with the number of days on the forum, and then t-testing whether the correlations are significantly different from zero). In support of the hypothesis, the results show an increase in similarity, as indicated by decreasing deviation scores (Figure 2). The mean correlation coefficient between this measure and time on the forum was -.0086, which is significant, t(11749) = -3.77, p < 0.001. (pg 85)
    • ForumAlignmentI think it is reasonable to consider this a measure of alignment
  • Because individuals form identities online and because we see this in the use of pronouns, we also expected to see tendencies of social influence and adaption. This effect was also found, such that individuals’ linguistic style became increasingly similar to other users’ linguistic style over time. Past research has shown that accommodation of communication style occurs automatically when people connect to people or groups they like (Giles & Ogay 2007; Ireland et al., 2011), but also that similarity in communicative style functions as cohesive glue within a group (Reid, Giles, & Harwood, 2005). (pg 86)
  • Still, the results could not confirm an increase in cognitive complexity. It is difficult to determine why this was not observed even though a general trend to conform to the linguistic style on the forum was observed. (pg 87)
    • This is what I would expect. As alignment increases, complexity, as expressed by higher dimensional thinking should decrease.
  • This idea would also be in line with previous research that has shown that expressing oneself decreases arousal (Garcia et al., 2016). Moreover, because the forum is not explicitly racist, individuals may have simply adapted to the social norms on the forum prescribing less negative emotional displays. Finally, a possible explanation for the decrease in negative emotional words might be that users who are very angry leave the forum, because of its non-racist focus, and end up in more hostile forums. An interesting finding that was not part of the hypotheses in the present research is that the third person plural category correlated positively with all four negative emotions categories, suggesting that people using for example ‘they’ express more negative emotions (pg 87)
  • In line with social identity theory (Tajfel & Turner, 1986), we also observe linguistic adaption to the group. Hence, our results indicate that processes of identity formation may take place online. (pg 87)

Influence of augmented humans in online interactions during voting events

Influence of augmented humans in online interactions during voting events

  • Massimo Stella (Scholar)
  • Marco Cristoforetti (Scholar)
  • Marco Cristoforetti (Scholar)
  • Abstract: Overwhelming empirical evidence has shown that online social dynamics mirrors real-world events. Hence, understanding the mechanisms leading to social contagion in online ecosystems is fundamental for predicting, and even manouvering, human behavior. It has been shown that one of such mechanisms is based on fabricating armies of automated agents that are known as social bots. Using the recent Italian elections as an emblematic case study, here we provide evidence for the existence of a special class of highly influential users, that we name “augmented humans”. They exploit bots for enhancing both their visibility and influence, generating deep information cascades to the same extent of news media and other broadcasters. Augmented humans uniformly infiltrate across the full range of identified clusters of accounts, the latter reflecting political parties and their electoral ranks.
  • Bruter and Harrison [19] shift the focus on the psychological in uence that electoral arrangements exert on voters by altering their emotions and behavior. The investigation of voting from a cognitive perspective leads to the concept of electoral ergonomics: Understanding optimal ways in which voters emotionally cope with voting decisions and outcomes leads to a better prediction of the elections. (pg 1)
  • Most of the Twitter interactions are from humans to bots (46%); Humans tend to interact with bots in 56% of mentions, 41% of replies and 43% of retweets. Bots interact with humans roughly in 4% of the interactions, independently on interaction type. This indicates that bots play a passive role in the network but are rather highly mentioned/replied/retweeted by humans. (pg 2)
  • bots’ locations are distributed worldwide and they are present in areas where no human users are geo-localized such as Morocco.  (pg 2)
  • Since the number of social interactions (i.e., the degree) of a given user is an important estimator of the in uence of user itself in online social networks [1722], we consider a null model fixing users’ degree while randomizing their connections, also known as configuration model [2324].  (pg 2)
  • During the whole period, bot bot interactions are more likely than random (Δ > 0), indicating that bots tend to interact more with other bots rather than with humans (Δ < 0) during Italian elections. Since interactions often encode the spread of a given content online [16], the positive assortativity highlights that bots share contents mainly with each other and hence can resonate with the same content, be it news or spam.  (pg 2)
  • Differently from previous works, where the semantic content of bots and humans differs in its emotional polarity [12], in here we nd that bots mainly repeat the same political content of human users, thus boosting the spreading of hashtags strongly related to the electoral process, such as hashtags referring to the government or to political victory, names of political parties or names of influential politicians (see also 3). (pg 4)
  • Frequencies of individual hashtags during the whole electoral process display some interesting shifts, reported in Table III (Top). For instance, the hashtag #exitpoll, indicating the electoral outcome, becomes 10000 times more frequent on the voting day than before March 4. These shifts indicate that the frequency of hashtags reflects real-world events, thus underlining the strong link between online social dynamics and the real-world electoral process. (pg 4)
  • TABLE II. Top influencers are mostly bots. Hubs characterize influential users and broadcasters in online social systems [17], hence we use degree rankings for identifying the most in uential users in the network. (pg 5)
  • bots are mostly influential nodes which tend to interact mostly with other bots rather than humans and, when they interact with human users, they preferentially target the most influential ones. (pg 5)
  • we first filter the network by considering only pair of users with at least one retweet, with either direction, because re-sharing content it is often a good proxy of social endorsement [21]. However, Retweets alone are not sufficient to wash out the noise intrinsic to systems like Twitter, therefore we apply a more selective restriction, by requiring that at least another social action – i.e., either mention or reply – must be present in addition to a retweet [12]. This restrictive selection allows one to filter out all spurious interactions among users with the advantage of not requiring any thresholding approach with respect to the frequency of interactions themselves. (pg 5)
  • The resulting network is what we call the social bulk, i.e. a network core of endorsement and exchange among users. By construction, information ows among users who share strong social relationships and are characterized by similar ideologies: in fact, when a retweet goes from one user to another one, both of them are endorsing the same content, thus making non-directionality a viable approach for representing the endorsement related to content sharing. (pg 5)
  • Fiedler partitioning
  • The relevant literature has used the term “cyborg” for identifying indistinctly bot-assisted human or human-assisted bot accounts generating spam content over social platforms such as Twitter [5, 35]. Here, we prefer to use the term \augmented human” for indicating specifically those human accounts exploiting bots for artificially increasing, i.e. augmenting, their in uence in online social platforms, analogously to physical augmentation improving human performances in the real world [36]. (pg 8)
  • Baseline social behavior is defined by the medians of the two observables, like shown in Fig. 6c. This map allows to easily identify four categories of individuals in the social dynamics: i) hidden in uentials, generating information cascades rapidly spreading from a large small number of followers; ii) in uentials, generating information cascades rapidly spreading from a large number of followers; iii) broadcasters, generating information cascades slowly spreading from a large number of followers; iv) common users, generating information cascades slowly spreading from a small number of followers. (pg 9)
  • Hidden influentials, known to be efficient spreaders in viral phenomena [45], are mostly humans: in this category falls the augmented humans, assisted by social bots to increase their online visibility. (pg 10)
  • We define augmented humans as human users having at least 50% + 1 of bot neighbours in the social bulk. We discard users having less than 3 interactions in the social bulk. (pg 10)
  • The most central augmented human in terms of number of social interactions is Utente01, which interacts with 2700 bots and 55 humans in the social bulk. (pg 10)
  • The above cascade analysis reveals that almost 2 out 3 augmented humans resulted playing an important role in the flow of online content: 67% of augmented humans were either influentials or hidden influentials or broadcasters. These results strongly support the idea that via augmentation even common users can become social influencers without having a large number of followers/friends but rather by recurring to the aid of either armies of bots (e.g., Utente01, an hidden in uential) or the selection of a few key helping bots. (pg 11)

Consensus and Cooperation in Networked Multi-Agent Systems

Consensus and Cooperation in Networked Multi-Agent Systems

Journal: Proceedings of the IEEE

  • Proceedings of the IEEE is the leading journal to provide an in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science. Consistently ranked as one of the top journals by Impact Factor, Article Influence Score and more, the journal serves as a trusted resource for engineers around the world

Authors:

Abstract:

  • This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis framework is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with non-local information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions. Simulation results are presented that demonstrate the role of small world effects on the speed of consensus algorithms and cooperative control of multi vehicle formations.

Notes:

  • In networks of agents (or dynamic systems), “consensus” means to reach an agreement regarding a certain quantity of interest that depends on the state of all agents. A “consensus algorithm” (or protocol) is an interaction rule that specifies the information exchange between an agent and all of its (nearest) neighbors on the network (pp 215)
    • In my work, this is agreement on heading and velocity
  • Graph Laplacians are an important point of focus of this paper. It is worth mentioning that the second smallest eigenvalue of graph Laplacians called algebraic connectivity quantifies the speed of convergence of consensus algorithms. (pp 216)
  • More recently, there has been a tremendous surge of interest among researchers from various disciplines of engineering and science in problems related to multi-agent networked systems with close ties to consensus problems. This includes subjects such as consensus [26]–[32], collective behavior of flocks and swarms [19], [33]–[37], sensor fusion [38]–[40], random networks [41], [42], synchronization of coupled oscillators [42]–[46], algebraic connectivity of complex networks [47]–[49], asynchronous distributed algorithms [30], [50], formation control for multi-robot systems [51]–[59], optimization-based cooperative control [60]–[63], dynamic graphs [64]–[67], complexity of coordinated tasks [68]–[71], and consensus-based belief propagation in Bayesian networks [72], [73]. (pp 216)
    • That is a dense lit review. How did they order it thematically?
  • A byproduct of this framework is to demonstrate that seemingly different consensus algorithms in the literature [10], [12]–[15] are closely related. (pp 216)
  • To understand the role of cooperation in performing coordinated tasks, we need to distinguish between unconstrained and constrained consensus problems. An unconstrained consensus problem is simply the alignment problem in which it suffices that the state of all agents asymptotically be the same. In contrast, in distributed computation of a function f(z), the state of all agents has to asymptotically become equal to f(z), meaning that the consensus problem is constrained. We refer to this constrained consensus problem as the f-consensus problem. (pp 217)
    • Normal exploring/flocking/stampeding is unconstrained. Herding adds constraint, though it’s dynamic. The variables that have to be manipulated in the case of constraint to result in the same amount of consensus are probably what’s interesting here. Examples could be how ‘loud’ does the herder have to be? Also, how ‘primed’ does the population have to be to accept herding?
  • …cooperation can be informally interpreted as “giving consent to providing one’s state and following a common protocol that serves the group objective.” (pp 217)
  • Formal analysis of the behavior of systems that involve more than one type of agent is more complicated, particularly, in presence of adversarial agents in noncooperative games [79], [80]. (pp 217)
  • The reason matrix theory [81] is so widely used in analysis of consensus algorithms [10], [12], [13], [14], [15], [64] is primarily due to the structure of P in (4) and its connection to graphs. (pp 218)
  • The role of consensus algorithms in particle based flocking is for an agent to achieve velocity matching with respect to its neighbors. In [19], it is demonstrated that flocks are networks of dynamic systems with a dynamic topology. This topology is a proximity graph that depends on the state of all agents and is determined locally for each agent, i.e., the topology of flocks is a state dependent graph. The notion of state-dependent graphs was introduced by Mesbahi [64] in a context that is independent of flocking. (pp 218)
    • They leave out heading alignment here. Deliberate? Or is heading alignment just another variant on velocity
  • Consider a network of decision-making agents with dynamics ẋi = ui interested in reaching a consensus via local communication with their neighbors on a graph G = (V, E). By reaching a consensus, we mean asymptotically converging to a one-dimensional agreement space characterized by the following equation: x1 = x2 = … = x (pp 219)
  • A dynamic graph G(t) = (V, E(t)) is a graph in which the set of edges E(t) and the adjacency matrix A(t) are time-varying. Clearly, the set of neighbors Ni(t) of every agent in a dynamic graph is a time-varying set as well. Dynamic graphs are useful for describing the network topology of mobile sensor networks and flocks [19]. (pp 219)
  • GraphLaplacianGradientDescent(pp 220)
  • algebraic connectivity of a graph: The algebraic connectivity (also known as Fiedler value or Fiedler eigenvalue) of a graph G is the second-smallest eigenvalue of the Laplacian matrix of G.[1] This eigenvalue is greater than 0 if and only if G is a connected graph. This is a corollary to the fact that the number of times 0 appears as an eigenvalue in the Laplacian is the number of connected components in the graph. The magnitude of this value reflects how well connected the overall graph is. It has been used in analysing the robustness and synchronizability of networks. (wikipedia) (pp 220)
  • According to Gershgorin theorem [81], all eigenvalues of L in the complex plane are located in a closed disk centered at delta + 0j with a radius of delta, the maximum degree of a graph (pp 220)
    • This is another measure that I can do of the nomad/flock/stampede structures combined with DBSCAN. Each agent knows what agents it is connected with, and we know how many agents there are. Each agent row should just have the number of agents it is connected to.
  • In many scenarios, networked systems can possess a dynamic topology that is time-varying due to node and link failures/creations, packet-loss [40], [98], asynchronous consensus [41], state-dependence [64], formation reconfiguration [53], evolution [96], and flocking [19], [99]. Networked systems with a dynamic topology are commonly known as switching networks. (pp 226)
  • Conclusion: A theoretical framework was provided for analysis of consensus algorithms for networked multi-agent systems with fixed or dynamic topology and directed information flow. The connections between consensus problems and several applications were discussed that include synchronization of coupled oscillators, flocking, formation control, fast consensus in small-world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. The role of “cooperation” in distributed coordination of networked autonomous systems was clarified and the effects of lack of cooperation was demonstrated by an example. It was demonstrated that notions such as graph Laplacians, nonnegative stochasticmatrices, and algebraic connectivity of graphs and digraphs play an instrumental role in analysis of consensus algorithms. We proved that algorithms introduced by Jadbabaie et al. and Fax and Murray are identical for graphs with n self-loops and are both special cases of the consensus algorithm of Olfati-Saber and Murray. The notion of Perron matrices was introduced as the discrete-time counterpart of graph Laplacians in consensus protocols. A number of fundamental spectral properties of Perron matrices were proved. This led to a unified framework for expression and analysis of consensus algorithms in both continuous-time and discrete-time. Simulation results for reaching a consensus in small-worlds versus lattice-type nearest-neighbor graphs and cooperative control of multivehicle formations were presented. (pp 231)

Schooling as a strategy for taxis in a noisy environment

Schooling as a strategy for taxis in a noisy environment

Journal: Evolutionary Ecology: Evolutionary Ecology is a conceptually oriented journal of basic biology at the interface of ecology and evolution. The journal publishes original research, reviews and discussion papers dealing with evolutionary ecology, including evolutionary aspects of behavioral and population ecology. The objective is to promote the conceptual, theoretical and empirical development of ecology and evolutionary biology; the scope extends to all organisms and systems. Research papers present the results of empirical and theoretical investigations, testing current theories in evolutionary ecology.

Author: Daniel Grunbaum: My research program seeks to establish quantitative relationships between short-term, small-scale processes, such as individual movement behaviors, and their long-term, large-scale population level effects, such as population fluxes and distributions.

Abstract

  • A common strategy to overcome this problem is taxis, a behaviour in which an animal performs a biased random walk by changing direction more rapidly when local conditions are getting worse.
    • Consider voters switching from Bush->Obama->Trump
  • Such an animal spends more time moving in right directions than wrong ones, and eventually gets to a favourable area. Taxis is ineffcient, however, when environmental gradients are weak or overlain by `noisy’ small-scale fluctuations. In this paper, I show that schooling behaviour can improve the ability of animals performing taxis to climb gradients, even under conditions when asocial taxis would be ineffective. Schooling is a social behaviour incorporating tendencies to remain close to and align with fellow members of a group. It enhances taxis because the alignment tendency produces tight angular distributions within groups, and dampens the stochastic effects of individual sampling errors. As a result, more school members orient up-gradient than in the comparable asocial case. However, overly strong schooling behaviour makes the school slow in responding to changing gradient directions. This trade-off suggests an optimal level of schooling behaviour for given spatio-temporal scales of environmental variations.
    • This has implications for everything from human social interaction to ANN design.

Notes

  • Because limiting resources typically have `patchy’ distributions in which concentrations may vary by orders of magnitude, success or failure in finding favourable areas often has an enormous impact on growth rates and reproductive success. To locate resource concentrations, many aquatic organisms display tactic behaviours, in which they orient with respect to local variations in chemical stimuli or other environmental properties. (pp 503)
  • Here, I propose that schooling behaviours improve the tactic capabilities of school members, and enable them to climb faint and noisy gradients which they would otherwise be unable to follow. (pp 504)
  • Schooling is thought to result from two principal behavioural components: (1) tendencies to move towards neighbours when isolated, and away from them when too close, so that the group retains a characteristic level of compactness; and (2) tendencies to align orientation with those of neighbours, so that nearby animals have similar directions of travel and the group as a whole exhibits a directional polarity. (pp 504)
    • My models indicate that attraction isn’t required, as long as there is a distance-graded awareness. In other words, you align most strongly with those agents that are closest.
  • I focus in this paper on schooling in aquatic animals, and particularly on phytoplankton as a distributed resource. However, although I do not examine them specifically, the modelling approaches and the basic results apply more generally to other environmental properties (such as temperature), to other causes of population movement (such as migration) and to other socially aggregating species which form polarized groups (such as flocks, herds and swarms). (pp 504)
  • Under these circumstances, the search of a nektonic filter-feeder for large-scale concentrations of phytoplankton is analogous to the behaviour of a bacterium performing chemotaxis. The essence of the analogy is that, while higher animals have much more sophisticated sensory and cognitive capacities, the scale at which they sample their environment is too small to identify accurately the true gradient. (pp 505)
    • And, I would contend for determining optimal social interactions in large groups.
  • Bacteria using chemotaxis usually do not directly sense the direction of the gradient. Instead, they perform random walks in which they change direction more often or by a greater amount if conditions are deteriorating than if they are improving (Keller and Segel, 1971; Alt, 1980; Tranquillo, 1990). Thus, on average, individuals spend more time moving in favourable directions than in unfavourable ones. (pp 505)
  • A bacterial analogy has been applied to a variety of behaviours in more complex organisms, such as spatially varying di€usion rates due to foraging behaviours or food-handling in copepods and larval ®sh (Davis et al., 1991), migration patterns in tuna (Mullen, 1989) and restricted area searching in ladybugs (Kareiva and Odell, 1987) and seabirds (Veit et al., 1993, 1995). The analogy provides for these higher animals a quantitative prediction of distribution patterns and abilities to locate resources at large space and time scales, based on measurable characteristics of small-scale movements. (pp 505)
  • I do not consider more sophisticated (and possibly more effective) social tactic algorithms, in which explicit information about the environment at remote points is actively or passively transmitted between individuals, or in which individual algorithms (such as slowing down when in relatively high concentrations) cause the group to function as a single sensing unit (Kils, 1986, described in Pitcher and Parrish, 1993). (pp 506)
    • This is something that could be easily added to the model. There could be a multiplier for each data cell that acts as a velocity scalar of the flock. That should have significant effects! This could also be applied to gradient descent. The flock of Gradient Descent Agents (GDAs) could have a higher speed across the fitness landscape, but slow and change direction when a better value is found by one of the GDAs. It occurs to me that this would work with a step function, as long as the baseline of the flock is sufficiently broad.
  • When the noise predominates (d <= 1), the angular distribution of individuals is nearly uniform, and the up-gradient velocity is near zero. In a range of intermediate values of d(0.3 <= d <= 3), there is measurable but slow movement up-gradient. The question I will address in the next two sections is: Can individuals in this intermediate signal-to-noise range with slow gradient-climbing rates improve their tactic ability by adopting a social behaviour (i.e. schooling)? (pp 508)
  • The key attributes of these models are: (1) a decreasing probability of detection or responsiveness to neighbours at large separation distances; (2) a social response that includes some sort of switch from attractive to repulsive interactions with neighbours, mediated by either separation distance or local density of animals*; and (3) a tendency to align with neighbours (Inagaki et al., 1976; Matuda and Sannomiya, 1980, 1985; Aoki, 1982; Huth and Wissel, 1990, 1992; Warburton and Lazarus, 1991; Grunbaum, 1994). (pp 508)
    • * Though not true of belief behavior (multiple individuals can share the same belief), for a Gradient Descent Agent (GDA), the idea of attraction/repulsion may be important.
  • If the number of neighbours is within an acceptable range, then the individual does not respond to them. On the other hand, if the number is outside that range, the individual turns by a small amount, Δθ3, to the left or right according to whether it has too many or too few of them and which side has more neighbours. In addition, at each time step, each individual randomly chooses one of its visible neighbours and turns by a small amount, Δθ4, towards that neighbour’s heading. (pp 508)
  • The results of simulations based on these rules show that schooling individuals, on average, move more directly in an up-gradient direction than asocial searchers with the same tactic parameters. Figure 4 shows the distribution of individuals in simulations of asocial and social taxis in a periodic domain (i.e. animals crossing the right boundary re-enter the left boundary, etc.). (pp 509)
  • Gradient Schooling
  • As predicted by Equation (5), asocial taxis results in a broad distribution of orientations, with a peak in the up-gradient (positive x-axis) direction but with a large fraction of individuals moving the wrong way at any given time (Fig. 5a,b). By comparison, schooling individuals tend to align with one another, forming a group with a tightened angular distribution. There is stochasticity in the average velocity of both asocial and social searchers (Fig. 5c). On average, however, schooling individuals move up-gradient faster and more directly than asocial ones. These simulation results demonstrate that it is theoretically possible to devise tactic search strategies utilizing social behaviours that are superior to asocial algorithms. That is, one of the advantages of schooling is that, potentially, it allows more successful search strategies under `noisy’ environmental conditions, where variations on the micro-scales at which animals sense their environment obscure the macro-scale gradients between ecologically favourable and unfavourable regions. (pp 510)
  • School-size effects must depend to some extent on the tactic and schooling algorithms, and the choices of parameters. However, underlying social taxis are the statistics of pooling outcomes of independent decisions, so the numerical dependence on school size may operate in a similar manner for many comparable behavioural schemes. For example, it seems reasonable to expect that, in many alternative schooling and tactic algorithms, decisions made collectively by less than 10 individuals would show some improvement over the asocial case but also retain much of the variability. Similarly, in most scenarios, group statistics probably vary only slowly with group size once it reaches sizes of 50-100. (pp 514)
  • when group size becomes large, the behaviour of model schools changes in character. With numerous individuals, stochasticity in the behaviour of each member has a relatively weaker effect on group motion. The behaviour of the group as a whole becomes more consistent and predictable, for longer time periods. (pp 514)
    • I think that this should be true in belief spaces as well. It may be difficult to track one person’s trajectory, but a group in aggregate, particularly a polarized group may be very detectable.
  • An example of group response to changing gradient direction shows that there can be a cost to strong alignment tendency. In this example, the gradient is initially pointed in the negative y-direction (Fig. 9). After an initial period of 5 time units, during which the gradient orients perpendicularly to the x-axis, the gradient reverts to the usual x-direction orientation. The school must then adjust to its new surroundings by shifting to climb the new gradient. This example shows that alignment works against course adjustment: the stronger the tendency to align, the slower is the group’s reorientation to the new gradient direction. This is apparently due to a non-linear interaction between alignment and taxis: asymmetries in the angular distribution during the transition create a net alignment flux away from the gradient direction. Thus, individuals that pay too much attention to neighbours, and allow alignment to overwhelm their tactic tendencies, may travel rapidly and persistently in the wrong direction. (pp 516)
    • So, if alignment (and velocity matching) are strong enough, the conditions for a stampede (group behavior with negative outcomes – in this case, less food) emerge
  • The models also suggest that there is a trade-off in strengthening tendencies to align with neighbours: strong alignment produces tight angular distributions, but increases the time needed to adjust course when the direction of the gradient changes. A reasonable balance seems to be achieved when individuals take roughly the same time to coalesce into a polarized group as they do to orient to the gradient in asocial taxis. (pp 518)
    • There is something about the relationship between explore and exploit in this statement that I really need to think about.
  • Social taxis is potentially effective in animals whose resources vary substantially over large length scales and for whom movements over these scales are possible. (pp 518)
    • Surviving as a social animal requires staying in the group. Since belief can cover wide ranges (e.g. religion), does there need to be a mechanism where individuals can harmonize their beliefs? From Social Norms and Other Minds The Evolutionary Roots of Higher Cognition :  Field research on primate societies in the wild and in captivity clearly shows that the capacity for (at least) implicit appreciation of permission, prohibition, and obligation social norms is directly related to survival rates and reproductive success. Without at least a rudimentary capacity to recognize and respond appropriately to these structures, remaining within a social group characterized by a dominance hierarchy would be all but impossible.
  • Interestingly, krill have been reported to school until a food patch has been discovered, whereupon they disperse to feed, consistent with a searching function for schooling. The apparent effectiveness of schooling as a strategy for taxis suggests that these schooling animals may be better able to climb obscure large-scale gradients than they would were they asocial. Interactive effects of taxis and sociality may affect the evolutionary value of larger groups both directly, by improving foraging ability with group size, and indirectly, by constraining alignment rates. (pp 518)
  • An example where sociality directly affects foraging strategy is forage area copying, in which unsuccessful fish move to the vicinity of neighbours that are observed to be foraging successfully (Pitcher et al., 1982; Ranta and Kaitala, 1991; Pitcher and Parrish, 1993). Pitcher and House (1987) interpreted area copying in goldfish as the result of a two-stage decision process: (1) a decision to stay put or move depending on whether feeding rate is high or low; and (2) a decision to join neighbours or not based upon whether or not further solitary searching is successful. Similar group dynamics have been observed in foraging seabirds (Porter and Seally, 1982; Haney et al., 1992).
  • Synchrokinesis depends upon the school having a relatively large spatial extent: part of a migrating school encounters an especially favourable or unfavourable area. The response of that section of the school is propagated throughout the school by alignment and grouping behaviours, with the result that the school as a whole is more effective at route-finding than isolated individuals. Forage area copying and synchrokinesis are distinct from social taxis in that an individual discovers and reacts to an environmental feature or resource, and fellow group members exploit that discovery. In social taxis, no individual need ever have greater knowledge about the environment than any other — social taxis is essentially bound up in the statistics of pooling the outcomes of many unreliable decisions. Synchrokinesis and social taxis are complementary mechanisms and may be expected to co-occur in migrating and gradient-climbing schools. (pp 519)
  • For example, in the comparisons of taxis among groups of various sizes, the most successful individuals were in the asocial simulation, even though as a fraction of the entire population they were vanishingly small. (pp 519)
    • Explorers have the highest payoff for the highest risks

Speaker–listener neural coupling underlies successful communication

Speaker–listener neural coupling underlies successful communication (2010)

Greg J. Stephens

Lauren J. Silbert

Uri Hasson (HassonLab at Princeton)

Abstract:

  • Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker’s spatiotemporal brain activity to model listeners’ brain activity and found that the speaker’s activity is spatially and temporally coupled with the listener’s activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener’s brain activity mirrors the speaker’s activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker–listener coupling, the greater the understanding. We argue that the observed alignment of production- and comprehension-based processes serves as a mechanism by which brains convey information.
    • This seems to be the root article for neural coupling. It seems to be an area of vigorous study, with lots of work coming out from the three authors.
    • The study design is also really good.

Notes

  • In this study we directly examine the spatial and temporal coupling between production and comprehension across brains during natural verbal communication. (pp 14425)
  • Using fMRI, we recorded the brain activity of a speaker telling an unrehearsed real-life story and the brain activity … (n = 11) of a listener listening to the recorded audio of the spoken story, thereby capturing the time-locked neural dynamics from both sides of the communication. Finally, we used a detailed questionnaire to assess the level of comprehension of each listener. (pp 14425)
  • …because communication unfolds over time, this coupling will exhibit important temporal structure. In particular, because the speaker’s production-based processes mostly precede the listener’s comprehension-based processes, the listener’s neural dynamics will mirror the speaker’s neural dynamics with some delay. Conversely, when listeners use their production system to emulate and predict the speaker’s utterances, we expect the opposite: the listener’s dynamics will precede the speaker’s dynamics. (pp 14425)
  • To analyze the direct interaction of production and comprehension mechanisms, we considered only spatially local models that measure the degree of speaker–listener coupling within the same Talairach location. (pp 14426)
  • we also observed significant speaker–listener coupling in a collection of extralinguistic areas known to be involved in the processing of semantic and social aspects of the story (19), including the precuneus, dorsolateral prefrontal cortex, orbitofrontal cortex, striatum, and medial prefrontal cortex. (pp 14426)
  • In agreement with previous work, the story evoked highly reliable activity inmany brain areas across all listeners (8, 11, 12) (Fig. 2B, yellow). We note that the agreement with previous work is far from assured: the story here was both personal and spontaneous, and was recorded in the noisy environment of the scanner. The similarity in the response patterns across all listeners underscores a strong tendency to process incoming verbal information in similar ways. A comparison between the speaker–listener and the listener–listenermaps reveals an extensive overlap (Fig. 2B, orange). These areas include many of the sensory related, classic linguistic-related and extralinguistic-related brain areas, demonstrating that many of the areas involved in speech comprehension (listener–listener coupling) are also aligned during communication (speaker–listener coupling). (pp 14426)
  • To test whether the extensive speaker–listener coupling emerges only when information is transferred across interlocutors, we blocked the communication between speaker and listener. We repeated the experiment while recording a Russian speaker telling a story in the scanner, and then played the story to non–Russian speaking listeners (n = 11). In this experimental setup, although the Russian speaker is trying to communicate information, the listeners are unable to extract the information from the incoming acoustic sounds. Using identical analysis methods and statistical thresholds, we found no significant coupling between the speaker and the listeners or among the listeners. At significantly lower thresholds we found that the non–Russian-speaking listener–listener coupling was confined to early auditory cortices. This indicates that the reliable activity in most areas, besides early auditory cortex, depends on a successful processing of the incoming information, and is not driven by the low-level acoustic aspects of the stimuli. (pp 14426)
  • Neural Coupling
    • In my model, the anticipation is modeled by the alignment and velocity, but others come to similar conclusions. It may be a way of dealing with noisy environments. Which would be another way of saying group dynamics with incomplete information.
  • Our analysis also identifies a subset of brain regions in which the activity in the listener’s brain precedes the activity in the speaker’s brain. The listener’s anticipatory responses were localized to areas known to be involved in predictions and value representation (pp 14428)
  • Such findings are in agreement with the theory of interactive linguistic alignment (1). According to this theory, production and comprehension become tightly aligned on many different levels during verbal communication, including the phonetic, phonological, lexical, syntactic, and semantic representationsAccordingly, we observed neural coupling during communication at many different processing levels, including low-level auditory areas (induced by the shared input), production-based areas (e.g., Broca’s area), comprehension based areas (e.g., Wernicke’s area and TPJ), and high-order extralinguistic areas (e.g., precuneus and mPFC) that can induce shared contextual model of the situation(34). Interestingly, some of these extralinguistic areas are known to be involved in processing social information crucial for successful communication, including, among others, the capacity to discern the beliefs, desires, and goals of others.(pp 14429)

 

The Group Polarization Phenomenon

The Group Polarization Phenomenon

David G. Myers

Helmut Lamm

Experiments exploring the effects of group discussion on attitudes, jury decisions, ethical decisions, judgments, person perceptions, negotiations, and risk taking (other than the choice-dilemmas task) are generally consistent with a “group polarization” hypothesis, derived from the risky-shift literature. Recent attempts to explain the phenomenon fall mostly into one of three theoretical approaches: (a) group decision rules, especially majority rule (which is contradicted by available data), (b) interpersonal comparisons (for which there is mixed support), and (c) informational influence (for which there is strong support). A conceptual scheme is presented which integrates the latter two viewpoints and suggests how attitudes develop in a social context.

  • Pictures may be important as part of an argument. Need to be able to support that.
  • This polarization concept should also be distinguished from a related concept, extremization. Whereas polarization refers to shifts toward the already preferred pole, extremization has been used to refer to movement away from neutrality, regardless of direction. Since all instances of group polarization are instances of extremization, but not vice versa, extremization may be easier to demonstrate than polarization. (pp 603)
  • For convenience we have organized these studies into seven categories: attitudes, jury decisions, ethical decisions, judgments, person perceptions, negotiation behavior, and risk measures other than the choice dilemmas. This categorization is admittedly somewhat arbitrary. (pp 604)
  • In other studies, however, it is possible to infer the direction of initial preferences. Robinson (1941) conducted lengthy discussions of two attitudes. On attitude toward war, where students were initially quite pacifistic, there was a nonsignificant shift to even more pacifism following discussion. On attitude toward capital punishment, to which students were initially opposed, there was a significant shift to even stronger opposition. (pp 604)
  • Varying the stimulus materials. Myers and Kaplan (1976) engaged their subjects in discussion of stimulus materials which elicited a dominant predisposition of guilty or not guilty. After discussing traffic cases in which the defendants were made to appear as low in guilt, the Subjects Were even more definite in their judgments of innocence and more lenient in recommended punishment. After discussing “high-guilt” cases, the subjects polarized toward harsher judgments of guilt and punishment. (pp 605)
  • Group composition studies. Vidmar composed groups of jurors high or low in dogmatism. The high-dogmatism juries shifted toward harsher sentences following discussion, and the low-dogmatism groups shifted toward more lenient sentences, despite the fact that the high- and low-dogmatism juries did not differ in their predeliberation judgments. (pp 606)
  • Main and Walker (1973) observed that these constitutionality decisions were also more libertarian in the group condition (65% versus 45%). Although a minority of the single-judge decisions were prolibertarian, Walker and Main surmised that the preexisting private values of the judges were actually prolibertarian and that their decisions made alone were compromised in the face of antilibertarian public pressure. Their private values were then supposedly released and reinforced in the professional group context (pp 606)
  • From what we have been able to perceive thus far, the process is an interesting combination of rational persuasion, sheer social pressure, and the psychological mechanism by which individual perceptions undergo change when exposed to group discussion (pp 606)
  • Myers (1975) also used a faculty evaluation task. The subjects responded to 200 word descriptions of “good” or “bad” faculty with a scale judgment and by distributing a pay increase budget among the hypothetical faculty. As predicted by the group polarization hypothesis, good faculty were rated and paid even more favorably after the group interaction, and contrariwise for the bad faculty. (pp 608)
  • in general, the work on person perception supports the group polarization hypothesis, especially when the stimulus materials are more complex than just a single adjective. (pp 608)
  • Myers and Bach (1976) compared the conflict behavior of individuals and groups, using an expanded prisoner’s dilemma matrix cast in the language of a gas war. There was no difference in their conflict behavior (both individuals and groups were highly noncooperative). But on postexperimental scales assessing the subjects’ evaluations of themselves and their opponents, individuals tended to justify their own behavior, and groups were even more inclined toward self-justification. This demonstration of group polarization supports Janis’s (1972) contention that in situations of intergroup conflict, group members are likely to develop a strengthened belief in the inherent morality of their actions.  (pp 608)
  • Skewness cannot account for group polarization. This is particularly relevant to the majority rule scheme, which depends on a skewed distribution of initial choices. On choice dilemmas, positively skewed distributions (i.e., with a risky majority) should produce risky shift, and negatively skewed distributions should yield a conservative shift. Several findings refute this prediction. (pp 612)
  • Shifts in the group median, although slightly attenuated, are not significantly smaller than shifts in the group mean (pp 612)
  • Group shift has also been shown to occur in dyads (although somewhat reduced), where obviously there can be no skewness in the initial responses (pp 612)
  • while group decision models may be useful in other situations in which discussion is minimal or absent and the task is to reach agreement (e.g., Lambert, 1969), the models (or at least the majority rule model stressed in this analysis) are not a sufficient explanation of the group polarization findings we are seeking to explain. There are still a variety of other decision schemes that can be explored and with other specific tasks. But clearly, group induced shift on choice dilemmas is something more than a statistical artifact. (pp 612)
  • Interpersonal Comparisons theory suggests that a subject changes when he discovers that others share his inclinations more than he would have supposed, either because the group norm is discovered to be more in the preferred direction than previously imagined or because the subject is released to more strongly act out his preference after observing someone else who models it more extremely than himself. This theory, taken by itself, suggests that relevant new information which emerges during the discussion is of no consequence. Group polarization is a source effect, not a message effect. (pp 614)
    • This is very close to the flocking theory where one agent looks at the alignment and velocity of nearby agents.
  • Differences between self, presumed other, and ideal scores. One well-known and widely substantiated assumption of the interpersonal comparisons approach is the observation from choice-dilemmas research that if, after responding, the subjects go back over the items and guess how their average peer would respond and then go back over the items a third time and indicate what response they would actually admire most, they tend to estimate the group norm as more neutral than their own initial response and their ideal as more extreme (pp 613)
  • Lamm et al. (1972) have also shown that not only do subjects indicate their ideal as more extreme than their actual response, but they also suspect that the same is true of their peers. The tendency of people to perceive themselves as more in what they consider to be the socially desirable direction than their average peer extends beyond the choice dilemmas (see Codol, Note 13). For example, most businessmen believe themselves to be more ethical than the average businessman (Baumhart, 1968), and there is evidence that people perceive their own views as less prejudiced than the norm of their community (Lenihan, Note 14). (pp 613)
  • The tendency to perceive others as “behind” oneself exists only when the self response is made prior to estimating the group norm (McCauley, Kogan, & Teger, 1971; Myers, 1974). Evidently it is after one has decided for himself that there is then a tendency to consider one’s action as relatively admirable (by perceiving the average person as less admirable than oneself). (pp 613)
  • it has been reliably demonstrated that subjects perceive other persons who have responded more extremely than themselves (in the direction of their ideal) as more socially desirable than persons who have not (Baron, Monson, & Baron, 1973; Jellison & Davis, 1973; Jellison & Riskind, 1970, 1971; Madaras & Bern, 1968). A parallel finding exists in the attitude literature (Eisinger & Mills, 1968): An extreme communicator on one’s side of an issue tends to be perceived as more sincere and competent than a moderate. (pp 614)
  • Burnstein, Vinokur, and Pichevin (1974) took an informational influence viewpoint and showed that people who adopt extreme choices are presumed to possess cogent arguments and are then presumably admired for their ability. They also demonstrated that subjects have much less confidence in others’ choices than in their own, suggesting that the tendency to perceive others as more neutral than oneself simply reflects ignorance about others’ choices (pp 614)
  • self-ideal difference scores are less affected by order of measurement than self versus perceived other differences (Myers, 1974)—suggest that the self-ideal discrepancy may be the more crucial element of a viable interpersonal comparisons approach. (pp 614)
  • One set of studies has manipulated the information about others’ responses by providing fake norms. More than a dozen separate studies all show that subjects will move toward the manipulated norm (see Myers, 1973) (pp 615)
    • Can’t find this paper, but herding!
  • Consistent with this idea, they observed that exposure to others’ choices produced shift only when subjects then wrote arguments on the item. If knowledge of others’ choices was denied or if an opportunity to rethink the item was denied, no shift occurred. (pp 615)
  • On the other hand, it may be reasoned that in each of the studies producing minimal or nonexistent shift after exposure to others’ attitudes, the subjects were first induced to bind themselves publicly to a pretest choice and then simply exposed to others’ choices. It takes only a quick recall of some classic conformity studies (e.g., Asch, 1956) to realize that this was an excellent procedure for inhibiting response change. (pp 615)
  • Bishop and Myers (1974) have formulated mathematical models of the presumed informational influence mechanisms. These models assume that the amount of group shift will be determined by three factors: the direction of each argument (which alternative it favors), the persuasiveness of each argument, and the originality of each argument (the extent to which it is not already known by the group members before discussion). In discussion, the potency of an argument will be zero if either the rated persuasiveness is zero (it is trivial or irrelevant) or if all group members considered the argument before discussion (pp 616)
  • the simple direction of arguments is such an excellent predictor of shift (without considering persuasiveness and originality), it is not easy to demonstrate the superiority of the models over a simple analysis of argument direction as undertaken by Ebbesen and Bowers (1974). (pp 617)
    • This supports the notion that alignment and heading, as used in the model may really be sufficient to model polarizing behavior
  • A group that is fairly polarized on a particular item before discussion is presumably already in general possession of those arguments which polarize a group. A less extreme group has more to gain from the expression of partially shared persuasive arguments. (pp 617)
  • Passive receipt of arguments outside an interactive discussion context generally produces reduced shift (e.g., Bishop & Myers, 1974; Burnstein & Vinokur, 1973; St. Jean, 1970; St. Jean & Percival, 1974). Likewise, listening to a group discussion generally elicits less shift than actual participation (pp 617)
    • There may be implications here with respect to what’s being seen and read on the news having a lower influence than items that are being discussed on social media. A good questions is at what point does the reception of information feel ‘interactive’? Is clicking ‘like enough? My guess is that it is.
  • Verbal commitment could produce the increased sense of involvement and certainty that Moscovici and Zavolloni (1969) believe to be inherent in group polarization. (pp 618)
    • This reinforces the point above, but we need to know what the minimum threshold of what can be considered ‘verbal commitment’.
  • By offering arguments that tend toward the outer limits of his range of acceptability, the individual tests his ideals and also presents himself favorably to the group since, as we noted earlier, extremity in the direction of the ideal connotes knowledgeability and competence. (pp 618)
  • Diagram (pp 619) PolarazationDiagram
  • Arguments spoken in discussion more decisively favor the dominant alternative than do written arguments. The tendency for discussion arguments to be one-sided is probably not equal for all phases of a given discussion. Studies in speech-communications (see Fisher, 1974) suggest that one-sided discussion is especially likely after a choice direction has implicitly emerged and group members mutually reinforce their shared inclination. (pp 619)
    • This review is pre IRC, and views writing as non-interactive. THis may not be true any more.
  • The strength of the various vectors is expected to vary across situations. In more fact-oriented judgment tasks (group problem solving tasks being the extreme case), the cognitive determinants will likely be paramount, although people will still be motivated to demonstrate their abilities. On matters of social preference, in which the social desirability of actions is more evident, the direct and indirect attitudinal effects of social motivation are likely to appear. The direct impact will occur in situations in which the individual has ideals that may be compromised by presumed norms but in which exposure to others’ positions informs him that his ideals are shared more strongly or widely than he would have supposed. These situations—in which expressed ideals are a step ahead of prior responses—will also tend to elicit discussion content that is biased toward the ideals. (pp 620)
  • What is the extent of small group influence on attitudes? McGuire (1969) noted, “It is clear that any impact that the mass media have on opinion is less than that produced by informal face-to-face communication of the person with his primary groups, his family, friends, co-workers, and neighbors (p. 231,).” (pp 220)