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
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Alignment in social interactions

Alignment in social interactions (2016)

Journal: Consciousness and Cognition, an International Journal, provides a forum for a natural science approach to the issues of consciousnessvoluntary control, and self. The journal features empirical research (in the form of articles) and theoretical reviews. The journal aims to be both scientifically rigorous and open to novel contributions.

Mattia Gallotti (Scholar):  Manager of The Human Mind Project at the School of Advanced Study of the University of London. I have a keen interest in academic management and governance, and I now consult on aspects of social innovation in the public sector.

Merle Theresa Fairhurst-MenuhinMerle is equally driven by a passion for art and science. Her days are split between work in cognitive neuroscience and exploring the rich repertoire of art song.

Chris Frith (Scholar):  I have been trying to delineate the mechanisms underlying the human ability to share representations of the world, for it is this ability that makes communication possible and allows us to achieve more than we could as individuals. We think that there are two major processes involved. The first is an automatic form of priming (sometimes referred to as contagion or empathy), whereby our representations of the world become aligned with those of the person with whom we are interacting. The second is a form of forward modelling, analogous to that used in the control of our own actions.

Abstract:

  • According to the prevailing paradigm in social-cognitive neuroscience, the mental states of individuals become shared when they adapt to each other in the pursuit of a shared goal. We challenge this view by proposing an alternative approach to the cognitive foundations of social interactions. The central claim of this paper is that social cognition concerns the graded and dynamic process of alignment of individual minds, even in the absence of a shared goal. When individuals reciprocally exchange information about each other’s minds processes of alignment unfold over time and across space, creating a social interaction. Not all cases of joint action involve such reciprocal exchange of information. To understand the nature of social interactions, then, we propose that attention should be focused on the manner in which people align words and thoughts, bodily postures and movements, in order to take one another into account and to make full use of socially relevant information.

Notes:

  • The concept of alignment has since evolved and is used to describe the multi-level, dynamic, and interactive mechanisms that underpin the sharing of people’s mental attitudes and representations in all kinds of social interactions (Dale, Fusaroli, & Duran, 2013). (pp 253)
  • The underlying justification for subsuming all these cases under the same mechanism is that cognition and action cannot be separated. The sharing of minds and bodies can then be conceptualized in terms of an integrated system of alignment, defined as the dynamic coupling of behavioural and/or cognitive states of two people (Dumas, Laroche, & Lehmann, 2014). (pp 253)
  • we are interested in the explanatory significance of alignment for a more general theory of social interaction, not in instrumental behaviour and/or alignment per se. (pp 254)
  • The central claim of this paper is that the alignment of minds, which emerges in social interactions, involves the reciprocal exchange of information whereby individuals adjust minds and bodies in a graded and dynamic manner. As these processes of alignment unfold, interacting partners will exchange information about each other’s minds and therefore act socially, whether or not a shared goal is in place. (pp 254)
  • In particular, in recent theoretical and empirical work on social cognition, reciprocity is increasingly recognized as a useful resource to capture the ‘‘jointness” of a joint action.Interpersonal understanding can be achieved by reading into one another’s mind reciprocally (Butterfill, 2013), and an explanation of the processes whereby the alignment of minds and bodies unfolds in space and time should involve an account of reciprocity (Zahavi & Rochat, 2015). In the process of a reciprocal exchange of information, individuals may adapt to varying degrees to one another. This is certainly the case in instances of temporal synchronisation and coordination in which physical alignment in time and space has been theorized to depend on cognitive models of adaptation (Elliott, Chua, & Wing, 2016Hayashi & Kondo, 2013Repp & Su, 2013) and thus on reciprocal interactions (D’Ausilio, Novembre, Fadiga, & Keller, 2015Keller, Novembre, & Hove, 2014Tognoli & Kelso, 2015). The behaviour of one player results in a change in behaviour of the other in a reciprocal way so as to achieve temporal synchrony. Interestingly, though not surprisingly, this reciprocal exchange of information results in physical alignment, which in turn has also been shown to result in greater degrees of affiliation and greater mental alignment (Hove & Risen, 2009Rabinowitch & Knafo-Noam, 2015Wiltermuth & Heath, 2009). Specifically, we suggest that, rather than a focus on the sharedness of the intended goal, we should attend to the graded exchange of information that creates alignment. The most social of interactions, in our formulation, are those in which ‘‘live” (‘‘online”, see Schilbach, 2014) information is exchanged dynamically (i.e. over time, across multiple points) bidirectionally and used to adapt behaviour and align with another (Jasmin et al., 2016). (pp 255)
  • Indeed, it is possible to have reciprocity and thus social interaction without cooperation. This would be the case, for example, in a competitive scenario in which the minds of the subjects are aligned at the appropriate level of description, and the sharing is essential to solve social dilemmas involving antagonistic behaviour (Bratman, 2014). In these exchanges, what is needed for the minds of the agents to attune to one another is that they adapt thoughts, bodily postures and movements, to take one another into account and reason as a team, even though the team might consist of competitive actors where none is aware that they are acting from the perspective of the same group and in the pursuit of some common goal (Bacharach, 2006). (pp 255)
  • fundamentally social nature has to do with the process whereby systems reciprocate thoughts and experiences, rather than with the endpoint i.e. the goal. It turns out that two features are often taken to be central to the process whereby interacting agents align minds and bodies. First, the interacting agents must be aware that they are doing something together with others. Second, the success of their joint performance is taken as a measure of how shared the participants’ goals are. (pp 255)
  • our suggestion is that what matters for the relevant alignment of minds and bodies to occur is the reciprocal exchange of information, not awareness of the reciprocal exchange of information. (pp 255)
    • This is all that is needed for flocking to happen. It is the range of that exchange that determines the phase change from independent to flock to stampede. Trust is involved in the reciprocity too, I think
  • Becoming mutually aware that we are sharing attitudes, dispositions, bodily postures, perhaps goals, does not mean that the ‘jointness’ of our actions has become available to each of us for conscious report. Reciprocity of awareness is emphatically not the same as awareness of reciprocity. The process of reciprocally exchanging information and mutually adapting to one another need not necessarily result in any degree of shared awareness. (pp 256)
  • In animals, a signal, for example about the source of food, that is too weak for an individual fish to follow can be followed by a group through the simple rules of bodily alignment that create shoaling behaviour (Grunbaum, 1998). Shoaling behaviour can also be observed in humans (Belz, Pyritz, & Boos, 2013), who can achieve group advantage through more complex forms of adjustment than just bodily alignment. Pairs of participants trying to detect a weak visual signal can achieve a greater group advantage when they align the terms they use to report their confidence in what they saw (Fusaroli et al., 2012). Indeed, linguistic alignment at many levels can be observed in dialogue (Pickering & Garrod, 2004) and can improve comprehension (Adank, Hagoort, & Bekkering, 2010; Fusaroli et al., 2012). (pp 256)
  • Much research has been driven, so far, by the implicit goal of identifying optimal group performance as a proxy for mental alignment (Fusaroli et al., 2012), however, there is conceptual room and empirical evidence for arguing that optimal task performance is not a good index of mental alignment or ‘optimal sociality’. In other words, taking achievement of a shared goal as the paradigm of a social interaction leads to the binary conception of sociality according to which an interaction is either (optimally) social, or it is not. (pp 256)
    • This is a problem that I have with opinion dynamics models. Convergence on a particular opinion isn’t the only issue. There is a dynamic process where opinions fall in and out of favor. This is the difference between the contagion model, which is one way (uninfected->infected) and motion through belief space. The goal really doesn’t matter, except in a subset of cases (Though these may be very important)
  • Two systems can interact when they have access to information relating to each other (Bilek et al., 2015). There are different ways of exchanging information between systems and hence different types of interaction (Liu & Pelowski, 2014), but in everycase some kind of alignment occurs (Coey, Varlet, & Richardson, 2012Huygens, 1673). (pp 257)
  • Such offline interaction can be contrasted with the case of online social interactions, where both participants act. The distinction between offline and online social interaction tasks is now acknowledged as crucial for advancing our understanding of the cognition processes underlying social interaction (Schilbach, 2014). (pp 257)
  • In contrast to salsa, consider the case of tango in which movements are improvised and as such require constant, mutual adaptation (Koehne et al., 2015; Tateo, 2014). Tango dancers have access to information relating to each other and, by virtue of the task, they exchange information with one another across time in a reciprocal and bidirectional fashion. The juxtaposition of tango with salsa highlights a spectrum of degrees of mutual reciprocity, with a richer form of interaction and greater need for alignment in tango compared with salsa.
  • we will take reciprocity to be the primary requirement for social interactions. We suggest that reciprocity can be identified with a special kind of alignment, mutual alignment, involving adjustment in both parties to the interaction. However, not all cases of joint action lead to mutual alignment. It is important to distinguish this mutual alignment from other types of alignment, which do not involve a reciprocal exchange of information between the agents. (pp 257)
  • In contrast to salsa, consider the case of tango in which movements are improvised and as such require constant, mutual adaptation (Koehne et al., 2015Tateo, 2014). Tango dancers have access to information relating to each other and, by virtue of the task, they exchange information with one another across time in a reciprocal and bidirectional fashion. The juxtaposition of tango with salsa highlights a spectrum of degrees of mutual reciprocity, with a richer form of interaction and greater need for alignment in tango compared with salsa. (pp 257)
  • AlignmentInSocialInteractions(pp 258)
  • The biggest challenge currently facing philosophers and scientists of social cognition is to understand social interactions. We suggest that this problem is best approached at the level of processes of mental alignment rather than through joint action tasks based on shared goals, and we propose that the key process is one of reciprocal, dynamic and graded adaptation between the participants in the interaction. Defining social interactions in terms of reciprocal patterns of alignment shows that not all joint actions involve reciprocity and also that social interactions can occur in the absence of shared goals. This approach has two particular advantages. First, it emphasises the key point that interactions can only be fully understood at the level of the group, rather than the individual. The pooling together of individual mental resources generates results that exceed the sum of the individual contributions. But, second, our approach points towards the mechanisms of adaptation that must be occurring within each individual in order to create the interaction (Friston & Frith, 2015). (pp 259)
  • This picture of social interaction in terms of mental alignment suggests two important theoretical developments. One is about a possible way to characterize the idea that types of social interaction lie on a continuum of possible solutions. If we focus on the task or the shared goal being pursued by agents jointly, as the current literature suggests, then only limited subdivisions of types of interaction will emerge. If, however, our focus extends so as to integrate the nature of the interaction, conceived of in terms of information exchange, then we can arrive at a higher degree of resolution of the space in which social interaction lie. This will define a spectrum of types of interaction (not just offline versus online social cognition), suggesting a dimensional rather than a discrete picture. After all, alignment comes in degrees and a spectrum-like definition of sociality implies that there is a variety of forms of alignment and hence of interactions. (pp 269)
    • My work would indicate that meaningful transitions occur for Unaligned (pure explore), Complex (flocking), and Total (stampede).

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)

 

Sick echo chambers

Over the past year, I’ve been building a model that lets me look at how opinions evolve in belief space, much in the manner that flocks, herds and schools emerge in the wild.

CI_GP_Poster4a

Recently, I was Listening to BBC Business Daily this morning on Facebook vs Democracy:

  • Presenter Ed Butler hears a range of voices raising concern about the existential threat that social media could pose to democracy, including Ukrainian government official Dmytro Shymkiv, journalist Berit Anderson, tech investor Roger McNamee and internet pioneer Larry Smarr.

Roger McNamee and Larry Smarr in particular note how social media can be used to increase polarization based on emergent poles. In other words, “normal” opposing views can be amplified by attentive bad actors [page 24] with an eye towards causing generalized societal disruption.

My model explores emergent group interactions and I wondered if this adversarial herding in information space as it might work in my model.

These are the rough rules I started with:

  • Herders can teleport, since they are not emotionally invested in their belief space position and orientation
  • Herders appear like multiple individuals that may seem close and trustworthy, but they are actually a distant monolithic entity that is aware of a much larger belief space.
  • Herders amplify arbitrary pre-existing positions. The insight is that they are not herding in a direction, but to increase polarization
  • To add this to the model, I needed to do the following:
    • Make the size of the agent a function of the weight so we can see what’s going on
    • When in ‘herding mode’ the overall heading of the population is calculated, and the agent that is closest to that heading is selected to be amplified by our trolls/bot army.
    • The weight is increased to X, and the radius is increased to Y.
      • X represents AMPLIFICATION BY trolls, bots, etc.
      • A large Y means that the bots can swamp other, normally closer signals. This models the effect of a monolithic entity controlling thousands of bots across the belief space

Here’s a screenshot of the running simulation. There is an additional set of controls at the upper left that allow herding to be enables, and the weight of the influence to be set. In this case, the herding weight is 10. Though the screenshot shows one large agent shape, the amplified shape flits from agent to agent, always keeping closest to the average heading.

2017-10-28

The results are kind of scary. If I set the weight of the herder to 15, I can change the change the flocking behavior of the default to echo chamber.

  • Normal: No Herding
  • Herding weight set to 15, other options the same: HerdingWeight15

I did some additional tweaking to see if having highly-weighted herders ignore each other (they would be coordinated through C&C) would have any effect. It doesn’t. There is enough interaction through the regular populations to keep the alignment space reduced.

It looks like there is a ‘sick echo chamber’ pattern. If the borders are reflective, and the herding weight + influence radius is great enough, then a wall-hugging pattern will emerge.

The influence weight is sort of a credibility score. An agent that has a lot of followers, or says a lot of the things that I agree with has a lot of influence weight The range weight is reach.

Since a troll farm or botnet can be regarded as a single organization,  interacting with any one of the agents is really interacting with the root entity.  So a herding agent has high influence and high reach. The high reach explains the border hugging behavior.

It’s like there’s someone at the back of the stampede yelling YOUR’E GOING THE RIGHT WAY! KEEP AT IT! And they never go off the cliff because they are a swarm Or, it never goes of the cliff, because it manifests as a swarm.

A loud, distributed voice pointing in a bad direction means wall hugging. Note that there is some kind of floating point error that lets wall huggers creep off the edge.Edgecrawling

With a respawn border, we get the situation where the overall heading of the flock doesn’t change even as it gets destroyed as it goes over the border. Again, since the herding algorithm is looking at the overall population, it never crosses the border but influences all the respawned agents to head towards the same edge: DirectionPreserving

Who’d have thought that there could be something worse than runaway polarization?

Suppressing the Search Engine Manipulation Effect (SEME)

Suppressing the Search Engine Manipulation Effect (SEME)

  • Authors
    • Robert Epstein, (American Institute for Behavioral Research and Technology) Epstein and Robertson have found in multiple studies that search rankings that favor a political candidate drive the votes of undecided voters toward that candidate, an effect they call SEME (“seem”), the Search Engine Manipulation Effect.
    • Ronald Robertson (Northeastern University) I design experiments and technologies to explore the ways in which online platforms can influence the attitudes, beliefs, and behavior of individuals and groups. Currently, I am a PhD student in the world’s first Network Science PhD program at Northeastern University and am advised by Christo Wilson and David Lazer.
    • David Lazer (Northeastern University) professor of political science and computer and information science and the co-director of the NULab for Texts, Maps, and Networks
    • Christo Wilson (Northeastern University) Assistant Professor in the College of Computer and Information Science atNortheastern University. I am a member of the Cybersecurity and Privacy Institute and the Director of the BS in Cybersecurity Program in the College.

 

  • Abstract: A recent series of experiments demonstrated that introducing ranking bias to election-related search engine results can have a strong and undetectable influence on the preferences of undecided voters. This phenomenon, called the Search Engine Manipulation Effect (SEME), exerts influence largely through order effects that are enhanced in a digital context. We present data from three new experiments involving 3,600 subjects in 39 countries in which we replicate SEME and test design interventions for suppressing the effect. In the replication, voting preferences shifted by 39.0%, a number almost identical to the shift found in a previously published experiment (37.1%). Alerting users to the ranking bias reduced the shift to 22.1%, and more detailed alerts reduced it to 13.8%. Users’ browsing behaviors were also significantly altered by the alerts, with more clicks and time going to lower-ranked search results. Although bias alerts were effective in suppressing SEME, we found that SEME could be completely eliminated only by alternating search results – in effect, with an equal-time rule. We propose a browser extension capable of deploying bias alerts in real-time and speculate that SEME might be impacting a wide range of decision-making, not just voting, in which case search engines might need to be strictly regulated.
  • Introduction
    • Recent research has shown that society’s growing dependence on ranking algorithms leaves our psychological heuristics and vulnerabilities susceptible to their influence on an unprecedented scale and in unexpected ways
    • Experiments conducted on Facebook’s Newsfeed have demonstrated that subtle ranking manipulations can influence the emotional language people use
    • Similarly, experiments on web search have shown that manipulating election-related search engine rankings can shift the voting preferences of undecided voters by 20% or more after a single search
    • While “bias” can be ambiguous, our focus is on the ranking bias recently quantified by Kulshrestha et al. with Twitter rankings
    • Our results provide support for the robustness of SEME and create a foundation for future efforts to mitigate ranking bias. More broadly, our work adds to the growing literature that provides an empirical basis to calls for algorithm accountability and transparency [24, 25, 90, 91] and contributes a quantitative approach that complements the qualitative literature on designing interventions for ranking algorithms
    • Our results also suggest that proactive strategies that prevent ranking bias (e.g., alternating rankings) are more effective than reactive strategies that suppress the effect through design interventions like bias alerts. Given the accumulating evidence, we speculate that SEME may be impacting a wide range of decision-making, not just voting
  • Related Work
    • Order effects are among the strongest and most reliable effects ever discovered in the psychological sciences [29, 88]. These effects favorably affect the recall and evaluation of items at the beginning of a list (primacy) and at the end of a list (recency).
      • There does not seem to be an equivalent primacy effect in maps that I can find
    • online systems can: (1) provide a platform for constant, large-scale, rapid experimentation, (2) tailor their persuasive strategies by mining detailed demographic and behavioral profiles of users [1, 6, 9, 18, 121], and (3) provide users with a sense of control over the system that enhances their susceptibility to influence
      • Is this flocking from the flock’s perspective? Sort of an Ur-flock?
      • This is that Trust/Awareness equation again
    • A recent report involving 33,000 people found that search engines were the most trusted source of news, with 64% of people reporting that they trust search engines, compared to 57% for traditional media, 51% for online media, and 41% for social media [10]. Similarly, a 2012 survey by Pew found that 73% of search engine users report that “all or most of the information they find is accurate and trustworthy,” and 66% report that “search engines are a fair and unbiased source of information” [105].
    • Suggestions for fostering resistance can be broken down into two primary strategies: (1) providing forewarnings [43, 49] and (2) training and motivating people to resist [79, 120].
      • Interesting that alternate, non-ordered design approaches aren’t even mentioned
    • Part of the reason that forewarnings work is explained by psychological reactance theory [12], which posits that when people believe their intellectual freedom is threatened – by exposing an attempt to persuade, for example – they react in the direction opposite that of the intended one
    • In the context of online media bias, researchers have primarily explored methods for curbing the effects of algorithmic filtering and selective exposure [87, 96] rather than ranking bias [71]. In this vein, researchers have developed services that encourage users to explore multiple perspectives [97, 98] and browser extensions that gamify and encourage balanced political news consumption [19, 20, 86]. However, these solutions are somewhat impractical because they require users to adopt new services or exert additional effort.
  • Methods – Experiment Design
    • To construct biased search rankings we asked four independent raters to provide bias ratings of the webpages we collected on an 11-point Likert scale ranging from -5 “favors Cameron” to +5 “favors Miliband”. We then selected the 15 webpages that most strongly favored Cameron and the 15 that most strongly favored Miliband to create three bias groups
    • The query in the search engine was fixed as “UK Politics ‘David Cameron’ OR ‘Ed Miliband’”, and subjects could not reformulate it.
    • On top of assignment to a bias group, subjects were randomly assigned to one of three alert experiments.We drew from the literature on decision-making and design intervention to implement so-called debiasing strategies for improving decision-making in the presence of biased information [39, 78, 82]. Specifically, we constructed and placed alerts in the search results produced by our mock search engine that provided forewarnings with salient graphics, autonomony-supportive language, and details on the persuasive threat
  • Methods – Procedure
    • After providing informed consent and answering basic demographic questions
      • Do this and use this phrase!
    • Subjects then rated the two candidates on 10-point Likert scales with respect to their overall impression of each candidate, how much they trusted each candidate, and how much they liked each candidate. Subjects also indicated their likelihood of voting for one candidate or the other on an 11-point Likert scale where the candidates’ names appeared at opposite ends of the scale and 0 indicated no preference, as well as on a binary choice question where subjects indicated who they would vote for if the election were held today.
      • This is a good way to set up the game. People read the dilemma, formulate an initial solution and their level of commitment to it. They can choose to make it “public” as their first statement or to keep it private and display a “no opinion” initial statement
    • We asked: “While you were doing your online research on the candidates, did you notice anything about the search results that bothered you in any way?” and prompted subjects to explain what had bothered them in a free response format: “If you answered “yes,” please tell us what bothered you.” We did not directly ask subjects whether they had “noticed bias” to avoid the inflation of false positive rates that leading questions can cause
  • Methods – Participants
    • We recruited 3,883 subjects between April 28, 2015 and May 6, 2015 on Amazon’s Mechanical Turk (AMT; https://mturk.com), a subject pool frequently used by behavioral, economic, and social science researchers [8, 13, 102]. We excluded from our analysis subjects who reported an English fluency level of 5 or less (on a scale of 1 to 10) (n=26)
      • MTurk would be a good source of participants as well
  • Analysys – Search metrics
    • Utilizing Kolmogorov-Smirnov (K-S) tests of differences in distributions, we found significant differences in the patterns of time spent on the 30 webpages between subjects in the no alert experiment (correlation with ranking: Spearman’s ρ = -0.836, P <0.001) and the high alert experiment (ρ = -0.654, P <0.001) (K-S D = 0.467, P <0.01), and between subjects in the low alert experiment (ρ = -0.719, P <0.001) and the high alert experiment (K-S D = 0.400, P <0.01)
      • A way of looking for explore/exploit populations? And how fast can it be determined? Google uses a mechanism to stop an experiment once a confidence level is reached. Also, bootstrap would be good here
    • Similarly, we also found significant differences in the patterns of clicks that subjects made on the 30 webpages between subjects in the no alert experiment (ρ = -0.865, P <0.001) and the high alert experiment (ρ = -0.795, P <0.001) (K-S D = 0.500, P <0.001), and between subjects in the low alert experiment (ρ = -0.876, P <0.001) and the high alert experiment (K-S D = 0.367, P <0.05)
    • Among all conditions,we found that differences in the patterns of time and clicks on the individual rankings primarily emerged on the first SERP, but less so on the second, fourth, and fifth SERPs
  • Analysys – Attitude Shifts
    • we found that the mean shifts in candidate ratings for the bias groups significantly converged on the mean shift found in the neutral group as the level of detail in the alerts increased, with high alerts creating higher convergence than low alerts
      • As more diverse information is injected, populations compromise
  • Analysys – Vote Shifts
    • Vote Manipulation Power (VMP)is the percent change in the number of subjects, in the two bias groups combined, who indicated that they would vote for the candidate who was favored by their search rankings. That is, if x and x ′ subjects in the bias groups said they would vote for the favored candidate before and after conducting the search, respectively, then VMP = (x ′ − x)/x.
      • This could also be applied to the game to watch how votes for an outcome change over time. In the case of the game, new candidates can come into existence, so we need to watch for that.
  • Analysys – Bias Awareness
    • We found 8.1% of subjects that showed awareness of the bias in the no alert experiment, a figure identical to the 8.1% awareness rate found by Eslami et al. in their audit of Booking.com [37], and similar to the 8.6% of subjects who showed awareness in the original study [30]. The percentage of subjects showing bias awareness increased to 21.5% in the low alert experiment, and 23.4% in the high alert experiment.
  • Discussion
    • However, despite the additional suppression of the high alerts, the lowest VMP was found among the neutral group subjects: rankings alternating between favoring the two candidates prevented SEME.
      • This configuration forces users to “explore” more, within the context of a list affordance.
    • As with previous research on SEME [30], and with research on attitude change and influence more generally [3, 72, 120], we found that subjects vary in their susceptibility to SEME, as well as in their responsiveness to the alerts, based on their personal characteristics (Figure 6 and Figure 7 in the Appendix).
      • Explorer and exploiter populations?
    • As more people turn to the internet for political news [85, 115], designing systems that can monitor and suppress the effects of algorithm biases, like ranking bias, will play an increasingly important role in protecting the public’s psychological vulnerabilities.
      • And one of the big issues is finding bias at scale with domain independence
    • Real-time automated bias detection could potentially be achieved by utilizing a Natural Language Processing (NLP) approach. One could utilize opinions [75], sentiment [99], linguistic patterns [109], word associations [14], or recursive neural networks [59] with human-coded data to classify biased language.
      • Scale and domain problems.
  • Discussion – Awareness of bias
    • Awareness of ranking bias appears to suppress SEME only when it occurs in conjunction with a bias alert, perhaps because an alert is a kind of warning–inherently negative in nature.
      • According to Moscovici, an inherently negative construct should reduce polarization movement.
    • Awareness of ranking bias in the absence of bias alerts might increase VMP because people perceive the bias as a kind of social proof [111, 112], made all the more powerful because of the disproportionate trust people have in search rankings [10, 95, 105]. The user’s interpretation might be, “This candidate MUST be good, because even the search results say so.”

Some thoughts about trust and awareness

I had some more thoughts about how behavior patterns emerge from the interplay between trust and awareness. I think the following may be true:

  1. Trust is a social construct to deal with incomplete information. It’s a shortcut that essentially states “based on some set of past experiences, I will assume that this (now trusted) entity will behave in a predictable, reliable, and beneficial way for me”
  2. Awareness refers to how complete the knowledge of the information domain is. Completely aware indicates complete information. Unaware indicates not only absent information but no knowledge of the domain at all.
  3. Healthy behaviors emerge when trust and awareness are equivalent.
  4. Low trust and low awareness is reasonable. It’s like walking through a dark, unknown space. You go slow, bump into things, and adjust.
  5. Low trust and high awareness is paralytic.
  6. High trust and low awareness is reckless. Runaway conditions like echo chambers.
  7. Diversity is a mechanism for extending awareness, but it depends on trusting those who are different. That may be the essence of the explore/exploit dilemma.
  8. In a healthy group context, trust falls off as a function of awareness. That’s why we get flocking. That is the pattern that emerges when you trust more those who are close, while they in turn do the same, building a web of interaction. It’s kind of like interacting ripples?
  9. This may work for any collection of entities that have varied states that undergo change in some predictable way. If they were completely random, then awareness of the state is impossible, and trust should be zero.
    1. Human agent trust chains might proceed from self to family to friends to community, etc.
    2. Machine agent trust chains might proceed from self to direct connections (thumb drives, etc) to LAN/WAN to WAN
    3. Genetic agent trust chain is short – self to species. Contact is only for reproduction. Interaction would reflect the very long sampling times.
    4. Note that (1) is evolved and is based on incremental and repeated interactions, while (2) is designed and is based on arbitrary rules that can change rapidly. Genetics are maybe dealing with different incentives? The only issue is persisting and spreading (which helps in the persisting)
  10. Computer-mediated-communication disturbs this process (as does probably every form of mass communication) because the trust in the system is applied to the trust of the content. This can work in both ways. For example, lowering trust in the press allows for claims of Fake News. Raising the trust of social networks that channel anonymous online sources allows for conspiracy thinking.
  11. An emerging risk is how this affects artificial intelligence, given that currently high trust in the algorithms and training sets is assumed by the builders
    1. Low numbers of training sets mean low diversity/awareness,
    2. Low numbers of algorithms (DNNs) also mean low diversity/awareness
    3. Since training/learning is spread by update, the installed base is essentially multiple instances of the same individual. So no diversity and very high trust. That’s a recipe for a stampede of 10,000 self driving cars.