--- --- --- --- --- --- ---

terça-feira, 29 de dezembro de 2015

Processing power limits social group size: computational evidence for the cognitive costs of sociality

At http://rspb.royalsocietypublishing.org/content/280/1765/20131151.full.pdf+html

T. Dávid-Barrett, R. I. M. Dunbar
Published 26 June 2013.DOI: 10.1098/rspb.2013.1151

Abstract

Sociality is primarily a coordination problem. However, the social (or communication) complexity hypothesis suggests that the kinds of information that can be acquired and processed may limit the size and/or complexity of social groups that a species can maintain. We use an agent-based model to test the hypothesis that the complexity of information processed influences the computational demands involved. We show that successive increases in the kinds of information processed allow organisms to break through the glass ceilings that otherwise limit the size of social groups: larger groups can only be achieved at the cost of more sophisticated kinds of information processing that are disadvantageous when optimal group size is small. These results simultaneously support both the social brain and the social complexity hypotheses.

1. Introduction

Social living can take one of two forms: animals can form loose aggregations (exemplified by insect swarms and antelope herds, which are typically based on short-term advantages and whose persistence depends on immediate costs and benefits) or they can form congregations (exemplified by the bonded social groups of primates and some other mammals, including cetaceans, elephants and equids among others) whose advantages derive from long-term association, and whose persistence mainly depends on the trade-off between short- and long-term benefits and costs [1]. It has been suggested that this second kind of sociality is a kind of large-scale coordination problem that depends on bonding processes underpinned by more sophisticated cognitive mechanisms (associated with larger brains [2]). Indeed, the social brain hypothesis [2–7] implies that the size of group that can coordinate its behaviour is limited by the cognitive capacity (essentially, the neural processing capacity) that organisms can bring to bear on the problem.

Although there is considerable neuroanatomical evidence (at the individual as well as the species level) to support the social brain hypothesis in both primates [8] and specifically humans [9–12], the role that cognition plays in this remains unspecified. The recent resurgence of interest in the relationship between communication complexity and social complexity (the social complexity hypothesis) [13,14] offers a possible mechanism by postulating that (i) the complexity of information processing limits social group size and (ii) these communication competences depend on computationally expensive cognitive capacities.

We here use an agent-based model to show that the cognitive demands of coordination impose an upper limit on the size of social group that a species can maintain, but that costly increases in information processing, while disadvantageous when optimal group size is small, can allow the evolution of much larger groups providing there is sufficient benefit from doing so. In our model, the objective function is the size of group that can achieve effective social coordination, and we use a central processing unit's time required to achieve this objective function as an estimate of the cognitive demands of different information-processing strategies. In this, we assume that the size of the brain affects the speed and volume, and hence complexity, of decisions that can be made.

We use a novel coordination task for this. A pure coordination task differs from the conventional public good tasks in that there is no optimal solution, the sole point being to converge on some common behaviour, with the payoff simply reflecting the extent to which the members of the group converge. Conventional public good games involve what amounts to a trading relationship, whereas a coordination task of the kind we use simply requires agents to converge on some common solution. In this respect, it mirrors the common human case in which individuals adopt a set of shared cultural values, where the cultural icon or marker may be arbitrary and the value of the icon itself secondary to the fact that individuals are bound together in a cultural community, which in turn allows them to solve collective action problems more effectively [15,16]. Coordination problems of this kind may have been especially important in the context of the evolution of human sociality [16]. One important consequence of this conception is that, since agents do not pay or withhold some of their capital, there is no payoff for free-riding.

While coordination tasks can, of course, have a direct functional outcome (e.g. agreeing direction of travel to some desirable resource [17]), many coordination tasks in humans simply involve agreeing on cultural markers (or 'tags') to identify group members so as to enable collective action in the future [15,16]. In many of these cases, the coordination task can be quite arbitrary (agreeing on a common cultural icon or belief about the world [18–20], or even a common dialect [21,22]). Cultural convergence of this kind is not a functional end in itself, but rather provides the psychological basis for subsequently achieving a functional end (in some cases—but not always—reinforced through costly signalling [23,24] or costly punishment [25]). Signing up to the same cultural marker may signal acceptance of a set of cultural or moral values that acts as a cue of trustworthiness and willingness to reciprocate.

An important feature of our model is that agents are not panmictic, but rather, as in the real world, are constrained in the number of individuals with whom they can interact by the structure of the social network in which they are embedded [26–28]. The social networks of these species are characterized by dyadic relationships that require expensive maintenance (for instance, in grooming time) leading to long-term edge stability. Although panmixia has often been assumed in models of the evolution of behaviour [29,30], natural human populations are invariably structured and network dynamics are radically different in structured populations [31–33]. Our models map agents onto a network, and so limit the range of agents with whom they can interact.

2. Material and methods

The basic structure of our approach focuses on a group of n agents that face a coordination problem which requires behavioural synchrony, such that each agent has to do her part at the right time and in the right way for the group to be able to act as one (for a general overview of the model, see [31]). We use synchronization on a dial to capture this problem. This is a simple but widely used [31,34] device that stands for almost any coordination problem, though it most obviously relates to how a group decides on direction of travel. (We stress that agreeing a direction of travel is only one of many possible coordination tasks, many of which—as in agreeing on an arbitrary cultural icon as a marker of group identity—are in themselves only indirectly functional. Agreeing the direction of travel has the advantage of being particularly easy to model.) Each agent is first assigned an initial value between 0° and 360°, and, for convenience, we refer to this as its information value. One of these vectors is defined as the 'true information' and is the property of just one agent, while all the other agents are assigned a randomly distributed value. The agents are arranged in a random n-node network in which each agent is linked to k others. The agents' task is to synchronize their information values, which they do by comparing their respective values and then finding a consensus. Synchronization takes place via a set of random dyadic meetings, in which the agents exchange and update their information values. (For detailed mathematical definitions and derivation, see appendices.) After each meeting (and exchange of information), each agent calculates a weighted average of three bits of information: her original information, the partner's information and the information the partner received in her previous meeting. These meetings are repeated until, on average, each agent has taken part in τ meetings. At this point, the average distance between the agents' individual information and the true information isEmbedded Image where w and ω are weight matrices for the weights the agent uses for the partners' and the third party information, respectively, while ϕT,i is the information held by agent i at the end of the synchronization episode, ϕTI is the true information and T is the total number of meetings in the group: T = nτ/2.

Agents are assumed to be trying to get as close to the true information as possible, though they are not necessarily aware of what this value actually is. To do this, each of them estimates an 'optimal' set of weights using a memory of past information exchanges and a simple least-squares optimization function (which is similar to the way humans optimize [35]). Note that requiring the agents to converge on a single value (the true information) is not in itself a defining feature of the model: it is simply a heuristic device to force coordination while at the same time minimizing computational demand. Allowing the agents to find their own equilibrium leads to convergence in just the same way [31], but it invariably takes longer. Our concern is with the constraints that different communication and cognitive strategies place on how fast convergence (synchronization) occurs, and the limits that this imposes on the size of social groups.

In our analysis, agents have three possible ways of estimating the optimal weights, which correspond to increasing levels of cognitive demand. Model F = 1 is the simplest: agents ignore both the third party information and the differences among their partners. In model F = 2, the agents ignore third party information, but recognize that there are differences (e.g. in reliability) among their partners. In model F = 3, the agents use both types of information. In calculating these weights, we varied the size of memory sample that the agents could use in their optimization. We used the processor time associated with each optimization act as an index of the cognitive demand of a strategy, and used this as a proxy for the amount of brain tissue needed in managing a strategy. This allowed us to introduce an implicit distance functionEmbedded Image where Embedded Image is the measured processor time, F is the index of the method used by the agents, and Embedded Image and Embedded Image are the 'optimal' weight matrices as estimated by the agents.

We assume that there is some threshold of synchronization efficiency, above which the group is deemed unable to perform the communal action, and below which the group is in sufficient behavioural synchrony to be able to act as one. Using this threshold, we can define the largest group that can be in synchrony as follows:Embedded Image where λ is the synchrony threshold.

3. Results

As might be expected, our simulations show that the maximum group size increases as calculation capacity increases for all the three models (figure 1). More importantly, however, figure 1 shows that for both models F = 2 and F = 3 there is a c*(F) such that n*(F − 1,c) > n*(F,c), for c < c* and n*(F − 1,c) < n*(F,c), for c > c*. In other words, the simplest model with the lowest computational demand allows groups to form, but these are constrained to relatively small sizes. There is some possibility of increasing group size by increasing computational capacity, but this option is capped. To achieve a significant further increase in group size, the agents must switch to a more complex information-processing strategy (model F = 2) that allows them to differentiate among their partners. Note that while this method is uneconomical for small groups, it yields an increase in group size if the calculation capacity is large enough. Just as in the simple model, a further increase in calculation capacity allows a further increase in group size, but it too hits an upper limit (albeit at a higher level). To move beyond this limit, the agents not only need larger computational capacities, but also have to add an additional information stream (model F = 3). This is the least economical method for small or middle-sized groups, but, by permitting third party information to be exchanged, it allows the group to cut through the glass ceiling imposed by the other two models and significantly raises the limit on group size.

Figure 1.

Limiting values for social group size n* as a function of the processor time costs Embedded Image required to achieve synchrony in an ecological objective, for three different cognitive strategies in an agent-based model with a structured network. F = 1 (dotted line): agents rely only on current information about agents they interact with. F = 2 (dashed line): agents take note of individual differences in the quality of information other agents offer. F = 3 (solid line): agents take note of individual differences between other agents and rely on third party information about each other received from other agents. Increasing calculation capacity allows larger groups in each of the three strategies for evaluating the quality of potential collaborators. However, each strategy has an upper limit (glass ceiling), and if groups need larger group size they have to switch to more sophisticated methods of information acquisition, and that necessitates an increase in the computational costs (i.e. brain size). (Parameters used: k = 4, τ = 20, λ = 11. The results are robust to these parameters.)

4. Discussion

It is important to note that the more complex strategies are highly disadvantageous when group size is small: indeed, the more complex the strategy, the more disadvantageous it is. Thus, the evolution of communicative and cognitive complexity is explicitly dependent on an ecological demand for large social groups: it is only when there is a need for large groups that the selection pressure will be sufficient to warrant the costs involved. This is driven by the demands of social coordination. If there is no requirement for coordination (in terms of the present model, optimal group size n*≈ 1 because organisms do not need to cooperate), then there is insufficient selection pressure to motivate either complex communication or investment in the large brains required to support the requisite cognitive abilities.

This suggests that complex communicative abilities, large social groups that involve social coordination and large brains will all be equally rare, as indeed seems to be the case [13]. Although we have used a very simple (and computationally easy to implement) objective function in the model (achieving synchrony in compass direction), it is important to be clear that our model is not limited to this particular context. Rather, our device stands as an abstraction for any behaviour that requires synchrony or coordination in order to maximize biological fitness, whether the fitness payoff is a direct or an indirect consequence of coordination along stable, expansive dyadic network edges. Direct fitness payoffs may arise from coordinated foraging or hunting (as in some social carnivores [36]), cooperative defence against predators or rival conspecifics (e.g. group territorial defence; as in many primates) or any other ecologically relevant behaviour that requires group members to synchronize or coordinate their behaviour in some way. However, in species characterized by multi-level social systems (e.g. elephants, some cetaceans, most primates [37] and humans [27,28]) where the higher level of organization functions to solve a collective action problem, the payoff may be indirect and mechanisms are needed to facilitate cooperation at group level. In these cases, solving a collective action problem is a two-step process: willingness to collaborate is established before the need to collaborate [38], and is often (but not necessarily always) signalled by some marker (or 'tag') of group membership.

Our model not only shows how cognition (processing power) could limit group size, but also sheds light on why some other species (for instance, herding mammals with panmictic group structure) use entirely different—and much simpler—coordination strategies. This, of course, does not preclude the possibility that bonded species such as humans might use simple heuristics to solve coordination tasks when the task demands are simple [39,40]. However, our analysis does raise the question as to why some species choose simple coordination methods associated with panmictic structure, while others resort to using more complex and costly cognition.

In sum, our analysis provides a formal mechanism for both the social brain hypothesis [3] and the social (or communicative) complexity hypothesis [13] by demonstrating that greater information-processing demands are reflected in greater cognitive (computational) costs, but that bringing these on stream can allow organisms to break through glass ceilings to significantly increase social group size. If size matters (large groups offer greater protection against predators [41], are more efficient for foraging [42] or win more territorial fights [43,44]), then there will be selection pressure to pay these costs. But the significant finding is that these costs are considerable and, when optimal group size is small, make the costs prohibitive. In these circumstances, simpler cognitive strategies are more profitable. In the limiting case, when there is no requirement for bonded relationships to ensure group stability through time, it is not worth paying the costs of complex cognition. This suggests that these kinds of more complex sociality will be relatively rare. Broadly speaking, this is what we see in the natural world [2].

Funding statement

This research is supported by a European Research Council Advanced grant to R.I.M.D.

Appendix A. The formal model

Let us define a group as a set of n agents that are interlinked in a connected network with each agent having a degree of k. Let G(n) denote the set of all possible such networks:Embedded Image where ei denotes the set of agents that agent i is connected to, and hence #ei denotes the length of this set and ρ(i,j) denotes the network distance between nodes i and j.

Let f denote the basic information-updating function, defined in the following way:Embedded Image Embedded Image Thus, the function f calculates a weighted average of two values on a dial, A and B, with the weights being, respectively, 1 and x. Thus, this function is a simple weighted average, and the only reason for the complication above is that it is on a dial. (It is possible to define f using trigonometric functions; however, the form is less intuitive that way.)

Let h denote the information-updating function with two information sources, defined as a nested f function:Embedded Image That is, the way the function h works is that first the function f calculates the weighted average of dial values A and B using the weight x, and dial values A and C using the weight y; and then the weight of the resulting two values is calculated using the weight y/x. (Note that due to the construction of the y/x weight, the parameters x and y do not have symmetric effect. This reflects the fact that B and C play different roles in the model, with B being the information the agent's meeting partner holds, and C being the third party information.)

Let ϕt,i denote the value of the information variable held by agent i at time t. Then, let TI ∼ U{1, 2, … ,n} denote a randomly selected agent that receives the true information, ϕTI ∼ U(0°,360°). The initial information values are defined the following way:Embedded Image That is, all agents receive a random initial value on a dial, except for the agent that was chosen as TI, which receives the true information, ϕTI.

Let πt,i denote the third party information, and let pt,i denote the index of the agent from which the third party information originates, both held by agent i at time t. The initial values for these two variables are set the following way:Embedded Image That is, the first third party information the agent receives from herself.

Let us define a synchronization event on a network g(n) ∈ G(n) as a series of T meetings among the agents, where a 'meeting' between two agents, a and b, is defined in the following way:Embedded Image That is, first, the agents a and b are chosen such that they are linked to each other. Second, the agents exchange and update their respective information, third party information, and third party agent index the following way:Embedded Image where wa,b and Embedded Image are the weights associated, respectively, with the information agent a receives from agent b and the third party information she receives from agent b.

These random meetings among the agents are repeated until t = T, where T = τ n/2. That is, on average each agent takes part in τ meetings.

Given the definition of the synchronization as a series of information exchanges on a graph, we can define a function that measures the average distance of the agent from the true information at the end of the synchronization process. Let d denote this measure in the following way:Embedded Image where w = {wi,j} and ω = {ωi,j} are the respective weight matrices.

(Note that d(n,w,ω) is independent of the arbitrary parameter ϕTI. And thus, this structure is why we employ the cumbersome use of a dial, rather than a one-dimensional range for the information variable. At the same time, if the weight parameters of the agents and the network structure are well behaved, the average distance, d, converges to zero as τ goes to infinity.)

Let us assume that a set of n agents, linked up in the network g(n) ∈ G(n), goes through a series of S synchronization events in a such a way that both the agent selected to be TI and the true information stays the same throughout, while the information variables of all non-TI agents acquire a new random initial value in each of the synchronization events. (All elements of the synchronization event are as defined earlier.) Then, let rs,t,i denote a memory record that agent i collects at meeting number t in the synchronization event s, defined the following way:Embedded Image That is, if an agent is party to a meeting, she records all the relevant information in her memory.

Then let Ri denote the entire memory of agent i, containing all her records for all of T meetings in all of S synchronization events:Embedded Image (For the initial values of wi,j and ωi,j used in the memory build-up, see below.)

The agents will select a sample from their memory, and choose a weight that will minimize their distance from the true information. Before we describe this process, however, we introduce a generic weight-optimization function. Let z(Q) denote the optimal weight given the sample Q, defined the following way:Embedded Image A1where E is the expectations operator. That is, z is the coefficient that minimizes the distance of the weighted value from the true information. (We used optimization on a linear range rather than on a dial, as the latter results in impractically long calculation time. Robustness checks showed, however, that there is no significant difference between the two measures in practice.)

Given the definition of optimization in (A 1) let us consider three different models of how the agents sample their memory, and calculate the optimal weights. Let F serve as an index of these models.

In the first model, F = 1, the agents ignore all third party information and do not differentiate among the other agents they encounter. Formally,Embedded Image andEmbedded Image andEmbedded Image where q denotes the size of the sample. That is, the agents only focus on primary information, and only differentiate self from not-self.

In the second model, F = 2, the agents still ignore the third party information, but they do differentiate among their partners. Formally,Embedded Image andEmbedded Image and (as before)Embedded Image That is, the agents still only focus on the primary information, but also use the information that lies in the differentiation between their partners. That is, by recognizing that their partners are different agents, they allow the weights they assign to their partners to reflect the difference in the quality of the information.

In the third model, F = 3, the agents use the third party information and differentiate among the other agents. Formally,Embedded Image and (as before)Embedded Image andEmbedded Image

Let c(F,q,n,g(n)) denote the processor time, which is the time it takes for the simulation computer to perform the optimization defined in (A 1). The processor time varies with the model index F, the sample size q, the group size n and the graph (network) that connects the agents.

Using this concept of processor time, let us define an implicit distance function δ the following way:Embedded Image A2where Embedded Image is the average measured processor time for all g(n) ∈ G(n). (That is, Embedded Image is a parameter that is measured during the simulation process.) Note that Embedded Image and g(n) allows the comparative measure, and thus the definition of the implicit distance function in (A 2). Also note that due to the construction of (A 2) the processor time c scales with the sample size q (although not linearly), allowing for a wider interpretation of computational complexity.

Using the δ function, we can define a maximum group size, denoted by n*, as follows:Embedded Image where λ is an arbitrary synchrony threshold. That is, n* is the largest number of agents that can perform a group synchronization (on an average k-degree network) in a way that the group reaches a threshold of synchrony, given the calculation capacity and method of optimization of the agents.

Proposition A.1.

Embedded Image for all F = 1, 2, 3.

Proposition A.2.

For both F = 2,3 there is a c*(F) such that n*(F − 1, c) > n*(F, c) for c < c* and n*(F − 1, c) < n*(F, c) for c > c*.

There is no algebraic solution to this problem, to our knowledge. However, a simulation result is obtained with τ = 20, k = 4 and λ = 11°. (Further computational parameters used were estimation limits in (A 2): −L ≤ β ≤ L, where we used L = 1000, and the number of synchronization events in the calculated memory S = 100. The initial values for the simulations' memory build up were wi,j = 1 and ωi,k = 0 for F = 1,2, and wi,j = 1 and ωi,k = 1 for F = 3, for all i, j, and k. For robustness checks, see the electronic supplementary material.)

For the calculated evidence for both propositions A.1 and A.2, see figure 1. (Note that due to the nature of numerical simulation, the inequalities of propositions A.1 and A.2 hold only weakly for some parameter ranges.)

Appendix B. A note on evolutionary dynamics

The model we present in this paper shows the group size limit of any network using the particular mode of information updating. One of the important questions concerning our model is whether there is a plausible evolutionary mechanism that would favour higher processing capacity on the individual level. Although our model is primarily concerned with constraints on the group size that would serve as limiting factors of any evolutionary process, we recognize that there might be a collective action problem (or public good problem) underlying the phenomenon we are modelling [45]. If the increased processing power is costly for the individual while aiding the group-level synchronization efficiency only marginally, then there might be an incentive for the individual to 'cheat' on the others by reducing its processing power, and thus free-ride on the others' higher capacities. If that was evolutionarily beneficial, there would be no group-level action at all [46].

Let us consider the set of graphs G(10) with k = 5, a group of 10 agents linked up in a random graph in such a way that each of them is connected to five others. Let us, for simplicity, assume that they are going through a series of synchronization events without any optimization, without differentiating among their partners and without third party information (i.e. w = 1 and ω = 0).

However, let us assume that the information reception of the agents contains noise in the following way:Embedded Image whereEmbedded Image and whereEmbedded Image That is, all agents apart from agent 1 receive the information from others within a bandwidth of ±30. For agent 1, the error level can vary in the range ɛ ∈ (0,180).

Let us assume that, just as in the main model of the paper, there is true information received by one randomly selected agent. After τ = 30 information exchanges per agent, we measure the average distance of the true information from agent 1, as well as from all the other agents. We found that both agent 1's distance and the other agents' distance from the true information increases with the error level of agent 1; however, the former relationship is more steep (figure 2a).

Figure 2.

The public good problem can be bypassed in the behavioural synchrony framework. (a) How the error level of agent 1 affects agent 1's and the other agents' respective distances from the true information. Solid line, agent 1; dashed line, mean for all the other agents. (b) A positive relationship between the cost parameter (β) and agent 1's optimal error level. Solid line, optimal error level of agent 1; dashed line, mean error level of all other agents. Note that if the cost is sufficiently low, then agent 1 would choose to have an error level lower than that of the rest of the group.

Now let us consider a payoff function for agent 1 that has the following form:Embedded Image B1where p is the total payoff, a is the payoff that agent 1 receives as a result of the collective action, β is a cost parameter and n = 10. Thus, the payoff function is of the following form: (payoff to focal individual) = (benefit of collective action) – (average group deviance) – (cost of cognitive investment) – (focal individual's deviation).

That is, the total payoff that the agent receives is dependent on the group being able to perform the collective action with efficiency, where the agent faces a cost for reducing her information's noise level, and she pays a penalty for being far from the true information. (Note that this is a particular example for a payoff function, where all the functional forms are linear, and the cost associated with group and individual efficiency is 1 in both cases. Trivially, the payoff from the collective action, a, can be any value, but unless a is big enough, p will not be positive and hence no action takes place.)

This formulation of the payoff function allows us to search for the optimal error level given the cost parameter:Embedded Image Simulation results show that—as expected—the distance from the true information increases with the error level of the focus agent for both the focus agent and the rest of the group, albeit with a different slope (figure 2a). This results in the effect that agent 1's optimal level of error increases as the cost of noise reduction increases (see figure 2b). Importantly, in some parameter ranges, the optimal error is smaller than that of the rest of the group. This suggests that if the focus agent's error level was the same as the rest of the group initially, reducing the error level would be beneficial for this individual, providing the route to evolutionary dynamics.

Note that there are two reasons why the public good problem does not arise in the context of a coordination game, in line with the observation that public good problems are essentially two-trait in nature [47]. First, ... ( more at http://rspb.royalsocietypublishing.org/content/280/1765/20131151.full.pdf+html )

  • Received May 7, 2013.
  • Accepted May 28, 2013.

References

  1. ↵
    1. Dunbar RIM,
    2. Shultz S
    . 2010 Bondedness and sociality. Behaviour 147, 775–803. doi:10.1163/000579510X501151 (doi:10.1163/000579510X501151)
  2. ↵
    1. Shultz S,
    2. Dunbar R
    . 2010 Encephalization is not a universal macroevolutionary phenomenon in mammals but is associated with sociality. Proc. Natl Acad. Sci. USA 107, 21 582–21 586. doi:10.1073/pnas.1005246107 (doi:10.1073/pnas.1005246107)
  3. ↵
    1. Dunbar RIM
    . 1992 Neocortex size as a constraint on group-size in primates. J. Hum. Evol. 22, 469–493. doi:10.1016/0047-2484(92)90081-J (doi:10.1016/0047-2484(92)90081-J)
    1. Dunbar RIM
    . 1998 The social brain hypothesis. Evol. Anthropol. 6, 178–190. doi:10.1002/(SICI)1520-6505(1998)6:5<178::AID-EVAN5>3.0.CO;2-8 (doi:10.1002/(SICI)1520-6505(1998)6:5<178::AID-EVAN5>3.0.CO;2-8)
    1. Barton RA
    . 1996 Neocortex size and behavioural ecology in primates. Proc. R. Soc. Lond. B 263, 173–177. doi:10.1098/rspb.1996.0028 (doi:10.1098/rspb.1996.0028)
    1. Byrne RB,
    2. Whiten A
    (eds) 1988 A Machiavellian intelligence: social complexity and the evolution of intellect in monkeys, apes and humans. Oxford, UK: Oxford University Press.
  7. ↵
    1. Barton RA,
    2. Dunbar RIM
    . 1997 Evolution of the social brain. In Machiavellian intelligence. II. Extensions and evaluations (eds Whiten A, Byrne RB), pp. 240–263. Cambridge, UK: Cambridge University Press.
  8. ↵
    1. Sallet J,
    2. et al.
    2011 Social network size affects neural circuits in macaques. Science 334, 697–700. doi:10.1126/science.1210027 (doi:10.1126/science.1210027)
  9. ↵
    1. Lewis PA,
    2. Rezaie R,
    3. Brown R,
    4. Roberts N,
    5. Dunbar RI
    . 2011 Ventromedial prefrontal volume predicts understanding of others and social network size. Neuroimage 57, 1624–1629. doi:10.1016/j.neuroimage.2011.05.030 (doi:10.1016/j.neuroimage.2011.05.030)
    1. Powell J,
    2. Lewis PA,
    3. Roberts N,
    4. Garcia-Finana M,
    5. Dunbar RIM
    . 2012 Orbital prefrontal cortex volume predicts social network size: an imaging study of individual differences in humans. Proc. R. Soc. B 279, 2157–2162. doi:10.1098/rspb.2011.2574 (doi:10.1098/rspb.2011.2574)
    1. Bickart KC,
    2. Wright CI,
    3. Dautoff RJ,
    4. Dickerson BC,
    5. Barrett LF
    . 2011 Amygdala volume and social network size in humans. Nat. Neurosci. 14, 163–164. doi:10.1038/nn.2724 (doi:10.1038/nn.2724)
  12. ↵
    1. Kanai R,
    2. Bahrami B,
    3. Roylance R,
    4. Rees G
    . 2012 Online social network size is reflected in human brain structure. Proc. R. Soc. B 279, 1327–1334. doi:10.1098/rspb.2011.1959 (doi:10.1098/rspb.2011.1959)
  13. ↵
    1. Freeberg TM,
    2. Dunbar RI,
    3. Ord TJ
    . 2012 Social complexity as a proximate and ultimate factor in communicative complexity. Phil. Trans. R. Soc. B 367, 1785–1801. doi:10.1098/rstb.2011.0213 (doi:10.1098/rstb.2011.0213)
  14. ↵
    1. Dunbar RIM
    . 2012 Bridging the bonding gap: the transition from primates to humans. Phil. Trans. R. Soc. B 367, 1837–1846. doi:10.1098/rstb.2011.0217 (doi:10.1098/rstb.2011.0217)
  15. ↵
    1. Dunbar RIM
    . 2008 Mind the gap: or why humans aren't just great apes. Proc. Br. Acad. 154, 403–423.
  16. ↵
    1. Ihara Y
    . 2011 Evolution of culture-dependent discriminate sociality: a gene-culture coevolutionary model. Phil. Trans. R. Soc. B 366, 889–900. doi:10.1098/rstb.2010.0247 (doi:10.1098/rstb.2010.0247)
  17. ↵
    1. Sigg H,
    2. Stolba A
    . 1981 Home range and daily march in a hamadryas baboon troop. Folia Primatol. (Basel) 36, 40–75. doi:10.1159/000156008 (doi:10.1159/000156008)
  18. ↵
    1. Tajfel H,
    2. Billig MG,
    3. Bundy RP,
    4. Flament C
    . 1971 Social categorization and intergroup behavior. Eur. J. Soc. Psychol. 1, 149–177. doi:10.1002/ejsp.2420010202 (doi:10.1002/ejsp.2420010202)
    1. Riolo RL,
    2. Cohen MD,
    3. Axelrod R
    . 2001 Evolution of cooperation without reciprocity. Nature 414, 441–443. doi:10.1038/35106555 (doi:10.1038/35106555)
  20. ↵
    1. Antal T,
    2. Ohtsuki H,
    3. Wakeley J,
    4. Taylor PD,
    5. Nowak MA
    . 2009 Evolution of cooperation by phenotypic similarity. Proc. Natl Acad. Sci. USA 106, 8597–8600. doi:10.1073/pnas.0902528106 (doi:10.1073/pnas.0902528106)
  21. ↵
    1. Nettle D,
    2. Dunbar RIM
    . 1997 Social markers and the evolution of reciprocal exchange. Curr. Anthropol. 38, 93–99. doi:10.1086/204588 (doi:10.1086/204588)
  22. ↵
    1. Cohen E
    . 2012 The evolution of tag-based cooperation in humans the case for accent. Curr. Anthropol. 53, 588–616. doi:10.1086/667654 (doi:10.1086/667654)
  23. ↵
    1. Sosis R,
    2. Alcorta C
    . 2003 Signaling, solidarity, and the sacred: the evolution of religious behavior. Evol. Anthropol. 12, 264–274. doi:10.1002/evan.10120 (doi:10.1002/evan.10120)
  24. ↵
    1. Sosis R,
    2. Kress HC,
    3. Boster JS
    . 2007 Scars for war: evaluating alternative signaling explanations for cross-cultural variance in ritual costs. Evol. Hum. Behav. 28, 234–247. doi:10.1016/j.evolhumbehav.2007.02.007 (doi:10.1016/j.evolhumbehav.2007.02.007)
  25. ↵
    1. Boyd R,
    2. Gintis H,
    3. Bowles S,
    4. Richerson PJ
    . 2003 The evolution of altruistic punishment. Proc. Natl Acad. Sci. USA 100, 3531–3535. doi:10.1073/pnas.0630443100 (doi:10.1073/pnas.0630443100)
  26. ↵
    1. Granovetter M
    . 1973 The strength of weak ties. Am. J. Sociol. 78, 1360–1380. doi:10.1086/225469 (doi:10.1086/225469)
  27. ↵
    1. Hamilton MJ,
    2. Milne BT,
    3. Walker RS,
    4. Burger O,
    5. Brown JH
    . 2007 The complex structure of hunter–gatherer social networks. Proc. R. Soc. B 274, 2195–2202. doi:10.1098/rspb.2007.0564 (doi:10.1098/rspb.2007.0564)
  28. ↵
    1. Zhou WX,
    2. Sornette D,
    3. Hill RA,
    4. Dunbar RIM
    . 2005 Discrete hierarchical organization of social group sizes. Proc. R. Soc. B 272, 439–444. doi:10.1098/rspb.2004.2970 (doi:10.1098/rspb.2004.2970)
  29. ↵
    1. Boyd R,
    2. Richerson PJ
    . 1985 Culture and the evolutionary process, pp. viii, 331. Chicago, IL: University of Chicago Press.
  30. ↵
    1. Sachs JL,
    2. Mueller UG,
    3. Wilcox TP,
    4. Bull JJ
    . 2004 The evolution of cooperation. Q. Rev. Biol. 79, 135–160. doi:10.1086/383541 (doi:10.1086/383541)
  31. ↵
    1. David-Barrett T,
    2. Dunbar RI
    . 2012 Cooperation, behavioural synchrony and status in social networks. J. Theor. Biol. 308, 88–95. doi:10.1016/j.jtbi.2012.05.007 (doi:10.1016/j.jtbi.2012.05.007)
    1. Dunbar RIM
    . 2011 Constraints on the evolution of social institutions and their implications for information flow. J. Institutional Econ. 7, 345–371. doi:10.1017/S1744137410000366 (doi:10.1017/S1744137410000366)
  33. ↵
    1. Gomez-Gardenes J,
    2. Reinares I,
    3. Arenas A,
    4. Floria LM
    . 2012 Evolution of cooperation in multiplex networks. Sci. Rep. 2, 620. doi:10.1038/srep00620 (doi:10.1038/srep00620)
  34. ↵
    1. Couzin ID,
    2. Krause J,
    3. Franks NR,
    4. Levin SA
    . 2005 Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516. doi:10.1038/nature03236 (doi:10.1038/nature03236)
  35. ↵
    1. Griffiths TL,
    2. Tenenbaum JB
    . 2006 Optimal predictions in everyday cognition. Psychol. Sci. 17, 767–773. doi:10.1111/j.1467-9280.2006.01780.x (doi:10.1111/j.1467-9280.2006.01780.x)
  36. ↵
    1. Kruuk H
    . 1972 The spotted hyena: a study of predation and social behavior, pp. xvi, 335. Chicago, IL: University of Chicago Press.
  37. ↵
    1. Hill RA,
    2. Bentley RA,
    3. Dunbar RIM
    . 2008 Network scaling reveals consistent fractal pattern in hierarchical mammalian societies. Biol. Lett. 4, 748–751. doi:10.1098/rsbl.2008.0393 (doi:10.1098/rsbl.2008.0393)
  38. ↵
    1. Harcourt AH
    . 1992 Coalitions and alliances: are primates more complex than non-primates? In Coalitions and alliances in humans and other animals (eds Harcourt AH, de Waal FBM), pp. 445–472. Oxford, UK: Oxford University Press.
  39. ↵
    1. Dyer JR,
    2. Ioannou CC,
    3. Morrell LJ,
    4. Croft DP,
    5. Couzin ID,
    6. Waters DA,
    7. Krause J
    . 2008 Consensus decision making in human crowds. Anim. Behav. 75, 461–470. doi:10.1016/j.anbehav.2007.05.010 (doi:10.1016/j.anbehav.2007.05.010)
  40. ↵
    1. Kearns M,
    2. Suri S,
    3. Montfort N
    . 2006 An experimental study of the coloring problem on human subject networks. Science 313, 824–827. doi:10.1126/science.1127207 (doi:10.1126/science.1127207)
  41. ↵
    1. Shultz S,
    2. Noe R,
    3. McGraw WS,
    4. Dunbar RIM
    . 2004 A community-level evaluation of the impact of prey behavioural and ecological characteristics on predator diet composition. Proc. R. Soc. Lond. B 271, 725–732. doi:10.1098/rspb.2003.2626 (doi:10.1098/rspb.2003.2626)
  42. ↵
    1. Reader SM,
    2. Hager Y,
    3. Laland KN
    . 2011 The evolution of primate general and cultural intelligence. Phil. Trans. R. Soc. B 366, 1017–1027. doi:10.1098/rstb.2010.0342 (doi:10.1098/rstb.2010.0342)
  43. ↵
    1. Adams ES
    . 1990 Boundary disputes in the territorial ant Azteca trigona: effects of asymmetries in colony size. Anim. Behav. 39, 321–328. doi:10.1016/S0003-3472(05)80877-2 (doi:10.1016/S0003-3472(05)80877-2)
  44. ↵
    1. Wilson ML,
    2. Wrangham RW
    . 2003 Intergroup relations in chimpanzees. Annu. Rev. Anthropol. 32, 363–392. doi:10.1146/annurev.anthro.32.061002.120046 (doi:10.1146/annurev.anthro.32.061002.120046)
  45. ↵
    1. Torney CJ,
    2. Levin SA,
    3. Couzin ID
    . 2010 Specialization and evolutionary branching within migratory populations. Proc. Natl Acad. Sci. USA 107, 20 394–20 399. doi:10.1073/pnas.1014316107 (doi:10.1073/pnas.1014316107)
  46. ↵
    1. West SA,
    2. Griffin AS,
    3. Gardner A
    . 2007 Evolutionary explanations for cooperation. Curr. Biol. 17, R661–R672. doi:10.1016/j.cub.2007.06.004 (doi:10.1016/j.cub.2007.06.004)
  47. ↵
    1. Brown SP,
    2. Taylor PD
    . 2010 Joint evolution of multiple social traits: a kin selection analysis. Proc. R. Soc. B 277, 415–422. doi:10.1098/rspb.2009.1480 (doi:10.1098/rspb.2009.1480)
  48. ↵
    1. Brosnan SF,
    2. Salwiczek L,
    3. Bshary R
    . 2010 The interplay of cognition and cooperation. Phil. Trans. R. Soc. B 365, 2699–2710. doi:10.1098/rstb.2010.0154 (doi:10.1098/rstb.2010.0154)
  49. ↵
    1. McNally L,
    2. Brown SP,
    3. Jackson AL
    . 2012 Cooperation and the evolution of intelligence. Proc. R. Soc. B 279, 3027–3034. doi:10.1098/rspb.2012.0206 (doi:10.1098/rspb.2012.0206)
View Abstract

  • DOWNLOAD PARCIAL. LARCEN, César Gonçalves. Mais uma lacônica viagem no tempo e no espaço: explorando o ciberespaço e liquefazendo fronteiras entre o moderno e o pós-moderno atravessando o campo dos Estudos Culturais. Porto Alegre: César Gonçalves Larcen Editor, 2011. 144 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. AGUIAR, Vitor Hugo Berenhauser de. As regras do Truco Cego. Porto Alegre: César Gonçalves Larcen Editor, 2012. 58 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LINCK, Ricardo Ramos. LORENZI, Fabiana. Clusterização: utilizando Inteligência Artificial para agrupar pessoas. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LARCEN, César Gonçalves. Pedagogias Culturais: dos estudos de mídia tradicionais ao estudo do ciberespaço em investigações no âmbito dos Estudos Culturais e da Educação. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120 p.
  • CALLONI, H.; LARCEN, C. G. From modern chess to liquid games: an approach based on the cultural studies field to study the modern and the post-modern education on punctual elements. CRIAR EDUCAÇÃO Revista do Programa de Pós-Graduação em Educação UNESC, v. 3, p. 1-19, 2014.
    http://periodicos.unesc.net/index.php/criaredu/article/view/1437


Postado por às Nenhum comentário:

Like Human Civilization? Thank Our Evil Side


By News Staff | November 25th 2015 07:33 AM


The speed and character of human dispersals changed significantly around 100,000 years ago, and our dark side deserves a thanksgiving for that; a new paper suggests that betrayals of trust were the missing link in understanding the rapid spread of our species around the world. 

Early species of hominin were limited in distribution to specific environments such as grasslands and open woodland. The expansion of Homo erectus out of Africa into Asia around 1.6 million years ago appears to have been caused by the need to find more large scale grasslands. By contrast, Neanderthals occupied cold and arid parts of Europe. All archaic species adapted slowly to new opportunities for settlement and were often deterred by environmental and climatic barriers.
Before 100,000 years ago, movement of archaic humans were largely governed by environmental events due to population increases or ecological changes. Afterwards populations spread with remarkable speed and across major environmental barriers. 

Dr. Penny Spikins, a senior lecturer in the Archaeology of Human Origins at University of York, speculates in Open Quaternary that neither population increase nor ecological changes provide an adequate explanation for patterns of human movement into new regions which began around 100,000 years ago. Instead, as commitments to others became more essential to survival, and human groups ever more motivated to identify and punish those who cheat, the 'dark' side of human nature also developed. Moral disputes motivated by broken trust and a sense of betrayal became more frequent and motivated early humans to put distance between them and their rivals. 

According to Spikins, the emotional bonds which held populations together in crisis had a darker side in heartfelt reactions to betrayal which we still feel today. Larger social networks made it easier to find distant allies with whom to start new colonies, and more efficient hunting technology meant that anyone with a grudge was a danger but it was human emotions which provided the force of repulsion from existing occupied areas which we do not see in other animals. 

And so dispersal into distant, risky and inhospitable areas became relatively more common compared with movements into already occupied regions. Most notably, the spread of modern human populations was not inhibited by biogeographical barriers. Populations moved into cold regions of Northern Europe, crossed significant deltas such as the Indus and the Ganges, deserts, tundra and jungle environment and even made significant sea crossings to reach Australia and the Pacific islands.

Spikins argues that betrayals of trust resulting from moral disputes were a significant reason for such risky dispersals into apparently unwelcoming environments with a desire to avoid physical harm from disgruntled former friends and allies being a key motivation. Offenders and any allies within their social network would feel driven to get out of... ( more at http://www.science20.com/news_articles/like_human_civilization_thank_our_evil_side-160582 )


Comments

well, it makes sense that once upon a time, if you didn;t like the area or the people, you could leave

now there is all this paperwork involved......
Nina Tryggvason (not verified) | 11/25/15 | 19:16 PM


  • DOWNLOAD PARCIAL. LARCEN, César Gonçalves. Mais uma lacônica viagem no tempo e no espaço: explorando o ciberespaço e liquefazendo fronteiras entre o moderno e o pós-moderno atravessando o campo dos Estudos Culturais. Porto Alegre: César Gonçalves Larcen Editor, 2011. 144 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. AGUIAR, Vitor Hugo Berenhauser de. As regras do Truco Cego. Porto Alegre: César Gonçalves Larcen Editor, 2012. 58 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LINCK, Ricardo Ramos. LORENZI, Fabiana. Clusterização: utilizando Inteligência Artificial para agrupar pessoas. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LARCEN, César Gonçalves. Pedagogias Culturais: dos estudos de mídia tradicionais ao estudo do ciberespaço em investigações no âmbito dos Estudos Culturais e da Educação. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120 p.
  • CALLONI, H.; LARCEN, C. G. From modern chess to liquid games: an approach based on the cultural studies field to study the modern and the post-modern education on punctual elements. CRIAR EDUCAÇÃO Revista do Programa de Pós-Graduação em Educação UNESC, v. 3, p. 1-19, 2014.
    http://periodicos.unesc.net/index.php/criaredu/article/view/1437


Postado por às Nenhum comentário:

quinta-feira, 17 de dezembro de 2015

Is Europe really going to ban teenagers from Facebook and the internet?


New European data protection rules would see companies require parental consent to handle data of those under 16, effectively blocking them from social media

european flagsTeenagers in Europe could be blocked from using Facebook, Whatsapp and other internet services and social media should European data protection laws that increase the age of consent to 16 be pushed through. Photograph: Georges Gobet/AFP/Getty Images

The European Parliament is set to vote on Tuesday on new rules that could see teenagers banned from internet services such as Facebook, social media, messaging services or anything that processes their data, without explicit consent from their parent or guardian.

The last-minute amendment to the new European data protection regulations would make it illegal for companies to handle the data of anyone aged 15 or younger, raising the legal age of digital consent to 16 from 13.

Companies wishing to allow those under 16 to use their services, including Facebook, Snapchat, Whatsapp and Instagram, will have to gain explicit consent from their legal guardian.

The draft law states: "The processing of personal data of a child below the age of 16 years shall only be lawful if and to the extent that such consent is given or authorised by the holder of parental responsibility over the child."

Companies such as Facebook currently allow users from the age of 13 to join their services. Their policies are based on the age of digital consent being 13, as defined by the US Children's Online Privacy Protection Act (Coppa) and similar laws in the EU, which afford those under 13 extra privacy protections.

Until recently, the draft European data protection bill, which is four years in the making, set the digital age of consent at 13, mirroring Coppa.


Experts against the change

The changes are equally opposed by technology companies and child-safety experts, who warned that the increased age of consent would make it very difficult for teenagers under 16 to use social media and other internet-based resources and services.

Janice Richardson, former coordinator of European Safer Internet network, and consultant to the United Nations' information technology body, the ITU and the Council of Europe said: "Moving the age from 13 to 16 represents a major shift in policy on which it seems there has been no public consultation.

"We feel that moving the requirement for parental consent from age 13 to age 16 would deprive young people of educational and social opportunities in a number of ways, yet would provide no more (and likely even less) protection."

Larry Magid, chief executive of ConnectSafely.org, said: "It will have the impact of banning a very significant percentage of youth and especially the most vulnerable ones who will be unable to obtain parental consent for a variety of reasons."

Not the first time those under 17 have been barred

The changes would legally stop teenagers from accessing social media, among other internet services, unless a parent or guardian consents, but it will likely not stop them from accessing the services.

Facebook required users to be 17 or older before 2006, when it was opened up to the public, but that did not stop teenagers from signing up. Most social media accounts request dates of birth on setting up accounts, but have no way to verify the information.

Unlike those adults signing up to over-18 services, such as adult entertainment sites, which often use a credit card as part of age verification, teenagers under 17 do not have verifiable age-based identification.

For the technology companies the biggest issue with the new rules would be policing them. Stopping teenagers under 16 from accessing messaging, social media and other sites would be very difficult. European legislators are no doubt under intense lobbying pressure to remove the age of consent change from the draft.

US technology firms, including Facebook and Google, have faced an increasingly tough European landscape over the recent years, coming under intense scrutiny over privacy and taxation practices.

The new... ( more at http://www.theguardian.com/technology/2015/dec/15/europe-ban-teenagers-facebook-internet-data-protection-under-16 )


  • DOWNLOAD PARCIAL. LARCEN, César Gonçalves. Mais uma lacônica viagem no tempo e no espaço: explorando o ciberespaço e liquefazendo fronteiras entre o moderno e o pós-moderno atravessando o campo dos Estudos Culturais. Porto Alegre: César Gonçalves Larcen Editor, 2011. 144 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. AGUIAR, Vitor Hugo Berenhauser de. As regras do Truco Cego. Porto Alegre: César Gonçalves Larcen Editor, 2012. 58 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LINCK, Ricardo Ramos. LORENZI, Fabiana. Clusterização: utilizando Inteligência Artificial para agrupar pessoas. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LARCEN, César Gonçalves. Pedagogias Culturais: dos estudos de mídia tradicionais ao estudo do ciberespaço em investigações no âmbito dos Estudos Culturais e da Educação. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120 p.
  • CALLONI, H.; LARCEN, C. G. From modern chess to liquid games: an approach based on the cultural studies field to study the modern and the post-modern education on punctual elements. CRIAR EDUCAÇÃO Revista do Programa de Pós-Graduação em Educação UNESC, v. 3, p. 1-19, 2014.
    http://periodicos.unesc.net/index.php/criaredu/article/view/1437


Postado por às Nenhum comentário:

quinta-feira, 19 de novembro de 2015

Beware of ads that use inaudible sound to link your phone, TV, tablet, and PC


Privacy advocates warn feds about surreptitious cross-device tracking.

by  - Nov 13, 2015 4:00pm BRST


Privacy advocates are warning federal authorities of a new threat that uses inaudible, high-frequency sounds to surreptitiously track a person's online behavior across a range of devices, including phones, TVs, tablets, and computers.

The ultrasonic pitches are embedded into TV commercials or are played when a user encounters an ad displayed in a computer browser. While the sound can't be heard by the human ear, nearby tablets and smartphones can detect it. When they do, browser cookies can now pair a single user to multiple devices and keep track of what TV commercials the person sees, how long the person watches the ads, and whether the person acts on the ads by doing a Web search or buying a product.

Cross-device tracking raises important privacy concerns, the Center for Democracy and Technology wrote in recently filed comments to the Federal Trade Commission. The FTC has scheduled a workshop on Monday to discuss the technology. Often, people use as many as five connected devices throughout a given day—a phone, computer, tablet, wearable health device, and an RFID-enabled access fob. Until now, there hasn't been an easy way to track activity on one and tie it to another.

"As a person goes about her business, her activity on each device generates different data streams about her preferences and behavior that are siloed in these devices and services that mediate them," CDT officials wrote. "Cross-device tracking allows marketers to combine these streams by linking them to the same individual, enhancing the granularity of what they know about that person."

The officials said that companies with names including SilverPush, Drawbridge, and Flurry are working on ways to pair a given user to specific devices. Adobe is also developing cross-device tracking technologies, although there's no mention of it involving inaudible sound. Without a doubt, the most concerning of the companies the CDT mentioned is San Francisco-based SilverPush.

CDT officials wrote:

Cross-device tracking can also be performed through the use of ultrasonic inaudible sound beacons. Compared to probabilistic tracking through browser fingerprinting, the use of audio beacons is a more accurate way to track users across devices. The industry leader of cross-device tracking using audio beacons is SilverPush. When a user encounters a SilverPush advertiser on the web, the advertiser drops a cookie on the computer while also playing an ultrasonic audio through the use of the speakers on the computer or device. The inaudible code is recognized and received on the other smart device by the software development kit installed on it. SilverPush also embeds audio beacon signals into TV commercials which are "picked up silently by an app installed on a [device] (unknown to the user)." The audio beacon enables companies like SilverPush to know which ads the user saw, how long the user watched the ad before changing the channel, which kind of smart devices the individual uses, along with other information that adds to the profile of each user that is linked across devices.

The user is unaware of the audio beacon, but if a smart device has an app on it that uses the SilverPush software development kit, the software on the app will be listening for the audio beacon and once the beacon is detected, devices are immediately recognized as being used by the same individual. SilverPush states that the company is not listening in the background to all of the noises occurring in proximity to the device. The only factor that hinders the receipt of an audio beacon by a device is distance and there is no way for the user to opt-out of this form of cross-device tracking. SilverPush's company policy is to not "divulge the names of the apps the technology is embedded," meaning that users have no knowledge of which apps are using this technology and no way to opt-out of this practice. As of April of 2015, SilverPush's software is used by 67 apps and the company monitors 18 million smartphones.

SilverPush's ultrasonic cross-device tracking was publicly reported as long ago as July 2014. More recently, the company received a new round of publicity when it obtained $1.25 million in venture capital. The CDT letter appears to be the first time the privacy-invading potential of the company's product has been discussed in detail. SilverPush officials didn't respond to e-mail seeking comment for this article.

Cross-device tracking already in use

The CDT letter went on to cite articles reporting that cross-device tracking has been put to use by more than a dozen marketing companies. The technology, which is typically not disclosed and can't be opted out of, makes it possible for marketers to assemble a shockingly detailed snapshot of the person being tracked.

"For example, a company could see that a user searched for sexually transmitted disease (STD) symptoms on her personal computer, looked up directions to a Planned Parenthood on her phone, visits a pharmacy, then returned to her apartment," the letter stated. "While previously the various components of this journey would be scattered among several services, cross-device tracking allows companies to infer that the user received treatment for an STD. The combination of information across devices not only creates serious privacy concerns, but also allows for companies to make incorrect and possibly harmful assumptions about individuals."

Use of ultrasonic sounds to track users has some resemblance to badBIOS, a piece of malware that a security researcher said used inaudible sounds to bridge air-gapped computers. No one has ever proven badBIOS exists, but the use of the high-frequency sounds to track users underscores the viability of the concept.

Now that SilverPush and others are using the technology, it's probably inevitable that it will remain in use in some form. But right now, there are no easy ways for average people to know if they're being... ( more at http://arstechnica.com/tech-policy/2015/11/beware-of-ads-that-use-inaudible-sound-to-link-your-phone-tv-tablet-and-pc/ )

  • DOWNLOAD PARCIAL. LARCEN, César Gonçalves. Mais uma lacônica viagem no tempo e no espaço: explorando o ciberespaço e liquefazendo fronteiras entre o moderno e o pós-moderno atravessando o campo dos Estudos Culturais. Porto Alegre: César Gonçalves Larcen Editor, 2011. 144 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. AGUIAR, Vitor Hugo Berenhauser de. As regras do Truco Cego. Porto Alegre: César Gonçalves Larcen Editor, 2012. 58 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LINCK, Ricardo Ramos. LORENZI, Fabiana. Clusterização: utilizando Inteligência Artificial para agrupar pessoas. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LARCEN, César Gonçalves. Pedagogias Culturais: dos estudos de mídia tradicionais ao estudo do ciberespaço em investigações no âmbito dos Estudos Culturais e da Educação. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120 p.
  • CALLONI, H.; LARCEN, C. G. From modern chess to liquid games: an approach based on the cultural studies field to study the modern and the post-modern education on punctual elements. CRIAR EDUCAÇÃO Revista do Programa de Pós-Graduação em Educação UNESC, v. 3, p. 1-19, 2014.
    http://periodicos.unesc.net/index.php/criaredu/article/view/1437


Postado por às Nenhum comentário:

Microsoft Invented Google Earth in the 90s Then Totally Blew It

WRITTEN BY JASON KOEBLER

November 13, 2015 // 09:30 AM EST

A screengrab of Terraserver from 1999. Image: Tom Barclay

The Earth fit inside a 45-foot by 25-foot Compaq computer in an office building in suburban Seattle. As the East Coast woke up Monday mornings, it would roar to life.

"The temperature in the room would go up 5 to 6 degrees, things would start banging around," Tom Barclay, the man tasked by Microsoft with putting the Earth inside a database, remembers. "You'd really marvel at it."

Terraserver could have, should have been a product that ensured Microsoft would remain the world's most important internet company well into the 21st century. It was the first-ever publicly available interactive satellite map of the world. The world's first-ever terabyte-sized database. In fact, it was the world's largest database for several years, and that Compaq was—physically speaking—the world's largest computer. Terraserver was a functional and popular Google Earth predecessor that launched and worked well before Google even thought of the concept. It let you see your house, from space.

So why aren't we all using Terraserver on our smartphones right now?

Probably for the same reason Microsoft barely put up a fight as Google outpaced it with search, email, browser, and just about every other consumer service. Microsoft, the corporation, didn't seem to care very much about the people who actually used Terraserver, and it didn't care about the vast amount of data about consumers it was gleaning from how they used the service.

"It was something we did to show off our software could do this, but the company didn't care about the information," Barclay told me. "Google was an information company first. They saw the value of the information."

***

An internal prototype of Terraserver. The final version was more complex. Image: Tom Barclay

From the outset, the plan was to make a database. Microsoft didn't really care what information it contained, it just had to be big. The biggest in the world, something that would test the scalability of Microsoft's SQL database products.

"We had been asked to work on a very large database, to test this next-generation database product," Barclay told me. "It turns out that finding both an interesting and real terabyte of data that wasn't encumbered in some way, that we had the permission to [distribute legally], was a challenging problem."

According to a USA Today article from June 22, 1998, the initial plan with Terraserver was to list every single transaction in the history of the New York Stock Exchange online and make it searchable. But that was only a half terabyte of data. Microsoft needed something larger.

In 1997, the United States Geological Survey was in the process of uploading greyscale satellite photos and other aerial images from its archives onto the internet. Hedy Rossmeisl of the USGS met with famed Microsoft computer scientist Jim Gray, and they started brainstorming. Wouldn't it be interesting, and perhaps useful, they thought, if someone put searchable satellite images on the internet?

Terraserver as it looked on launch day. Image: Tom Barclay

The timing was more-or-less perfect. The Cold War was over, which allowed spy satellite imagery to be declassified, no one was worried about terrorism in a pre-9/11 world, and, well, the average person was beginning to get the internet.

"We had imagery from maybe half of the country done digitally and we had some capabilities to deliver them, but not in a fast, accessible way," Rossmeisl told me. "I thought getting the data on the web was really important, and I wanted to help make it happen."

The images, along with some from recently declassified Russian military photos, totaled just over 2.3 terabytes. The idea for Terraserver was born.

Gray put Barclay, who Rossmeisl called "the brains of the project" in charge, and he got to coding. He was a database guy—Terraserver was the first website he'd ever made, and it was the first project he'd ever tried that had anything to do with mapping, which proved to be quite a challenge. Barclay quickly ran into an age-old cartography problem.

"It turns out that 'round Earth, flat monitor' is an enormous pain in the neck," Barclay said.

Image: Strebe/Wikimedia Commons

He decided that using a standard Mercator map projection, which is what you see in the image above, wouldn't work because it distorts the sizes of land masses as you move north and south on the projection. After trying a few things, Barclay came up with the idea of creating "mosaic" images that would be automatically generate based on where you're clicking on the map. Basically, the images given to Microsoft by USGS were stitched together but were then chopped into smaller images that could recenter themselves on cue.

A whitepaper published in 2000 explains how Barclay solved the projection issue. Images: Microsoft

"Originally, we hadn't done this. The very first demo we did, I chopped Bill Gates's house in half, which was not very good," he said. "We ended up with a progressive display that allowed people to drag and center the screen where they wanted it, and we computed zoomed out and zoomed in views."

These innovations proved to be revolutionary, and the "mosaic" strategy is now the "underpinning of Google Earth and Google Maps," Barclay said.

"I don't want to break my arm patting my back, but it's amazing how similar the current technologies are to what we did in 1998," he added. With the mapping problem solved, Terraserver went live, and the real fun began.

***

Image: Microsoft

I was 10 years old when Terraserver launched, and if I used it, I don't remember. Unfortunately, there's no way of using it today. Terraserver went offline in 2007, and Barclay spent most of his time working on Bing Maps. Microsoft periodically revived Terraserver from time to time even after 2007, but it's offline forever now. Barclay attempted to bring it back on a separate server for the purposes of this article, but said that the project proved too time consuming.

So while I don't remember Terraserver, it does seem like it made quite a splash when it launched. In addition to the USA Today article (more of a blurb, really), Terraserver also scored early stories from the New York Times and Newsweek, which worried about the system's potential privacy-invading potential (headline: "Surveillance in the sky").

Terraserver's initial specifications. Image: Microsoft

Microsoft held a launch event in New York City that Bill Gates attended. On the first day, 8 million people accessed the site, "millions more were rejected," according to a white paper published in 2000. By the end of the week, it was getting 30 million hits a day. Ultimately, the site settled down and served roughly 7 million people every day. It was more successful than anyone at Microsoft ever anticipated.

And that brings us to the dumbfounding thing about Terraserver, and about Microsoft. The reason, really, why I'm writing this article. In reading the white paper, it's astounding to see just how much information about general web behavior Microsoft was able to glean from the project, and it's astounding to see how it essentially blew it by looking at Terraserver as a novelty project rather than a potentially world-changing one.

Microsoft learned, maybe even before Google, that most search is local. If Terraserver didn't have images for people's hometowns, they got angry.

Image: Microsoft

"In the first year, I got 20,000 emails, and the vast majority of them said one of two things," Barclay said. "It was either 'I love Terraserver, I saw my house' or 'I hate Terraserver, I didn't see my house' We learned that 85 percent of all geospacial queries are local. They're looking for local search—they want to find whatever dry cleaner is around the corner, or where they could get fast food."

The entirety of the New York Times article about Terraserver's launch focuses on its utility as a database and all but ignores the possibility that it could serve as a method of collecting information about user habits.

"The project not only marks the creation of one whopper of a digital scrapbook, it also says something very big about Microsoft's effort to enter the database business, using as an opener a venture that can capture the public imagination," theTimes wrote. "Microsoft's strategy is to use Terraserver to prove that its software and operating system are suited to massive databases."

Image: Microsoft

It wasn't just that basic information, however. Microsoft also gleaned that "the internet is busiest on Mondays and Tuesdays" and that there was a "steady slide [in volume] from Wednesday through Friday." Saturdays and Sundays were half as busy as Mondays were. The 45-foot by 25-foot Compaq computer that stored the images would roar to life on Monday mornings as the East Coast woke up.

"The temperature in the room would go up 5 to 6 degrees around 9:30 AM on the East Coast, things would start banging around," Barclay said. "By 8 PM pacific time, you didn't have any traffic, because we didn't have any imagery in the Pacific Ocean."

None of this information was used by Microsoft, except as a way to determine when to perform maintenance on its servers or when to staff the server rooms. The only revenue Microsoft made directly off of Terraserver was on the sale of some of the satellite images, which you could buy and have mailed to your house for $9.95.

"In the science community, this technology took off, but as a business I could never get anyone at Microsoft to latch onto it," Barclay said. "There's definitely a little bit of frustration there."

***

It's easy to look at Terraserver as a missed opportunity for Microsoft to dominate the next era of computing, and it's hard to say why, exactly, the company decided to stop pouring resources into it. Current Microsoft representative declined to be interviewed for this article, and Jim Gray, Barclay's boss, was lost at sea in 2007.

It may be as simple as Barclay suggested: Microsoft didn't see itself as an information company, and the media was skeptical of its intentions had it decided to become one. In addition to the Newsweek article, the Chicago Sun Times ran an opinion piece in 2000 that questioned the company's motives with Terraserver.

"Some people are paranoid enough about Microsoft," Andy Ihnatko wrote in an article I accessed using LexisNexis. "How would these people react to discovering a Microsoft web server with an aerial photo of their house that's so good it shows the kiddie pool in the backyard?"

Other groups weren't as skittish. The most notable was Keyhole, which launched "Earth Viewer" in 2003 and used Terraserver as some of the underpinning of their technology. It sold the license to its Earth Viewer software for upwards of $600 annually to businesses and charged consumers $79 annually for a stripped down version of it. Google bought Keyhole in 2004, rebranded Earth Viewer as Google Earth in 2005 and... ( more at http://motherboard.vice.com/read/microsofts-terraserver-was-google-earth-before-there-was-google-earth )



  • DOWNLOAD PARCIAL. LARCEN, César Gonçalves. Mais uma lacônica viagem no tempo e no espaço: explorando o ciberespaço e liquefazendo fronteiras entre o moderno e o pós-moderno atravessando o campo dos Estudos Culturais. Porto Alegre: César Gonçalves Larcen Editor, 2011. 144 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. AGUIAR, Vitor Hugo Berenhauser de. As regras do Truco Cego. Porto Alegre: César Gonçalves Larcen Editor, 2012. 58 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LINCK, Ricardo Ramos. LORENZI, Fabiana. Clusterização: utilizando Inteligência Artificial para agrupar pessoas. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LARCEN, César Gonçalves. Pedagogias Culturais: dos estudos de mídia tradicionais ao estudo do ciberespaço em investigações no âmbito dos Estudos Culturais e da Educação. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120 p.
  • CALLONI, H.; LARCEN, C. G. From modern chess to liquid games: an approach based on the cultural studies field to study the modern and the post-modern education on punctual elements. CRIAR EDUCAÇÃO Revista do Programa de Pós-Graduação em Educação UNESC, v. 3, p. 1-19, 2014.
    http://periodicos.unesc.net/index.php/criaredu/article/view/1437


Postado por às Nenhum comentário:

terça-feira, 3 de novembro de 2015

Why One Scientist Killed A Bird That Hadn't Been Seen For Half A Century

 

October 25, 2015 | by Josh L Davis


photo credit: The bird hadn't been seen in over half a century, and even then it was only known from three specimens. Novitates Zoologicae. Volume 12/Wikimedia Commons

After searching for close to a century for the elusive bird, researchers have finally discovered what is to some considered the birding holy grail. First described from a single female specimen in the 1920s, the moustached kingfisher of the Solomon Islands wasn't again seen until another two females were collected in the 1950s. But a few weeks ago, ornithologist Chris Filardi, who had himself been searching for the bird for the past two decades, caught and took the first ever photographs of the moustached kingfisher. Many were in awe at this incredible find, until they found out that he killed it.

While on location, Filardi wrote in a blog about the moment he caught the elusive bird, in which his excitement is palpable: "When I came upon the netted bird in the cool shadowy light of the forest I gasped aloud, "Oh my god, the kingfisher." One of the most poorly known birds in the world was there, in front of me, like a creature of myth come to life." After almost a century of hiding in the shadowy forest, the beautiful orange and blue plumage of this bird had finally been brought into the light.

But what happened next has elicited quite a bit of controversy. Filardi then "collected" (read: killed) the bird. This has been the practice of biologists the world over for hundreds and hundreds of years, particularly during the Victorian era when tens of millions of rare and exotic animals were killed and stuffed for museums. These specimens have undoubtedly been invaluable for scientific research, and yet in more recent times – especially with the advent of photography and DNA analysis – this custom of killing the animal in question has fallen out of practice.

"This was neither an easy decision nor one made in the spur of the moment," wrote Filardi in a riposte to the mounting criticism. "This was not a 'trophy hunt.'" He argues that there are certain data that are simply unobtainable from just blood and DNA samples alone, including "a comprehensive set of material for molecular, morphological, toxicological, and plumage studies." The real question really comes down to whether such a rare and elusive species, which managed to evade capture for at least half a century and which the IUCNconsider endangered (though this could be due to lack of data), can take the killing of the individual male.

Filardi goes on to cover this point, claiming that "though sightings and information about the bird are rare in the ornithological community, the bird itself is not." From the surveys conducted in the forest during the expedition in which the bird was discovered, Filardi estimates that there are as many as 4,000 of them surviving in the rainforest. "On this trip, the real discovery was not finding an individual Moustached Kingfisher, but discovering that the world this species inhabits is still thriving in a rich and timeless way."

However, this hasn't stopped one particularly vocal critic. Marc Bekoff, professor of ecology and evolutionary biology at Colorado University, wrote in the Huffington Post that collecting specimens is "still the name of the game for some researchers: find a beautiful, unique, or rare animal and then kill it in the name of something or another to justify the unnecessary... ( more at http://www.iflscience.com/plants-and-animals/collection-specimens-scientific-study-still-necessary?utm_source=pocket&utm_medium=email&utm_campaign=pockethits )

  • DOWNLOAD PARCIAL. LARCEN, César Gonçalves. Mais uma lacônica viagem no tempo e no espaço: explorando o ciberespaço e liquefazendo fronteiras entre o moderno e o pós-moderno atravessando o campo dos Estudos Culturais. Porto Alegre: César Gonçalves Larcen Editor, 2011. 144 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. AGUIAR, Vitor Hugo Berenhauser de. As regras do Truco Cego. Porto Alegre: César Gonçalves Larcen Editor, 2012. 58 p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LINCK, Ricardo Ramos. LORENZI, Fabiana. Clusterização: utilizando Inteligência Artificial para agrupar pessoas. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120p. il.
  • DOWNLOAD GRATUÍTO. FREE DOWNLOAD. LARCEN, César Gonçalves. Pedagogias Culturais: dos estudos de mídia tradicionais ao estudo do ciberespaço em investigações no âmbito dos Estudos Culturais e da Educação. Porto Alegre: César Gonçalves Larcen Editor, 2013. 120 p.
  • CALLONI, H.; LARCEN, C. G. From modern chess to liquid games: an approach based on the cultural studies field to study the modern and the post-modern education on punctual elements. CRIAR EDUCAÇÃO Revista do Programa de Pós-Graduação em Educação UNESC, v. 3, p. 1-19, 2014.
    http://periodicos.unesc.net/index.php/criaredu/article/view/1437


Postado por às Nenhum comentário:
Assinar: Postagens (Atom)