Embedding management knowledge: repertoires and reservoirs

Predictions Results

Additional file 4: of Optimizing drug–target interaction prediction based on random walk on heterogeneous networks

110 Drugs predicted results with 3,519 targets

Belief Invasion.

<p>(a) The survival of cults and fringe groups depends on the coherence and strength of beliefs. We create a network with two communities with parameters <i>T</i> = 2.0, <i>I</i> = 0.09, and <i>J</i> = 2.0—putting the system in a regime where it will seek consensus. We vary the fraction of links that connect the cult community to the mainstream community, denoted <i>μ</i>. <i>e</i>_<i>o</i> is the number of social links between communities and ∑<i>k</i>_<i>i</i> is the total number of links in the cult (both shared and internal). The mainstream community attempts to convert the smaller cult. (b) At low <i>μ</i> the lack of exposure allows the cult to resist mainstream conversion. At higher <i>μ</i> there is sufficient exposure to the mainstream community to overcome the rigidity of the cult’s belief system. However, the process of conversion becomes more difficult as the cult’s beliefs become more coherent than mainstream beliefs. Cults are easily converted with highly coherent mainstream beliefs even at low exposure levels (black circles), while cults maintain their beliefs even at high exposure given low coherence of mainstream beliefs (red squares). Bars show standard deviation.</p

Cognitive-Social Belief Model.

<p>(a) Social models, such as the voter or Sznajd models, focus on the assimilation process through social pressure. Beliefs are usually simplified as independent states. (b) Cognitive models, such as the SKS model, focus on the interaction and coherence of beliefs of a single individual and how individuals make decisions and change their minds. The effect of social networks is often unaddressed. (c) Our model incorporates both forces, recognizing not only social pressures but also the connected nature of human beliefs. The social network acts as a conduit for belief transmission between individuals. We model a belief as a signed relationship between two concepts. We express the internal coherence of a network of such beliefs in terms of social balance theory where relationship triads can be either stable or unstable. The belief networks evolve over time as individuals decide whether to accept new beliefs transmitted by their peers.</p

Belief driven social instability.

<p>Strong societal consensus does not guarantee a stable society in our model. If major paradigm shifts occur and make individual belief systems incoherent, then society may become unstable. (a) The plot shows the evolution of social energy <i>E</i>^(<i>s</i>) over time. The system starts at consensus but with incoherent beliefs. After introducing a small perturbation, individuals leave consensus, searching for more coherent sets of beliefs, until society re-converges at a stable configuration. (b) Decreasing mean individual energies 〈<i>E</i>^(<i>i</i>)〉 over time illustrates individual stabilization during societal transition. (c) 〈<i>S</i>/<i>N</i>〉 is the fractional group size. As society is upset, the original dominant but incoherent belief system <i>S</i>_<i>o</i> (solid black) is replaced by an emerging coherent alternative <i>S</i>_<i>f</i> (dashed red).</p

Impact of the internal consistency of zealot beliefs.

<p>We define zealots as a group of individuals who share an identical, immutable belief system. Such belief systems can, however, vary in terms of their coherency. The dynamics of 〈<i>S</i>/<i>N</i>〉, the fractional size of the zealot population, over <i>ρ</i>_<i>o</i>, the density of zealots introduced into the population, reveals that zealots with more coherent beliefs can convert a population much more efficiently. In converting the whole population, the coherent set of beliefs (circles) require only less than half the density of zealots compared with incoherent beliefs (squares). Bars show standard error and <i>E</i>_<i>z</i> is the energy of the zealot’s belief-system. The simulations were run using <i>J</i> = 2.0, <i>T</i> = 2.0 and <i>I</i> = 90.0.</p

Community and belief rigidity.

<p>(a) Exposure determines conversion resistance when peer-influence (<i>I</i>) is strong. (b) Fringe groups can sustain their beliefs, even at a very high level of social exposure, with high levels of individual coherentism (<i>J</i>).</p