Influentials, Networks, and Public Opinion Formation
Journal of Consumer Research (in press, Dec. 2007)
Duncan J. Watts1
Peter Sheridan Dodds2
1Department of Sociology, Columbia University, New York, NY, 10025
(djw24@https://www.doczj.com/doc/6317439722.html,).
2Department of mathematics and statistics, University of Vermont, Burlington VT, 05404 (peter.dodds@https://www.doczj.com/doc/6317439722.html,).
A central idea in marketing and diffusion research is that influentials—a minority of individuals who influence an exceptional number of their peers—are important to the formation of public opinion. Here we examine this idea, which we call the “influentials hypothesis,” using a series of computer simulations of interpersonal influence processes. Under most conditions that we consider, we find that large cascades of influence are driven not by influentials, but by a critical mass of easily influenced individuals. Although our results do not exclude the possibility that influentials can be important, they suggest that the influentials hypothesis requires more careful specification and testing than it has received.
INTRODUCTION
In the 1940’s and 50’s, Paul Lazarsfeld, Elihu Katz, and colleagues (Katz and Lazarsfeld 1955; Lazarsfeld, Berelson and Gaudet 1968) formulated a breakthrough theory of public opinion formation that sought to reconcile the role of media influence with the growing realization that in a variety of decision-making scenarios, ranging from political to personal, individuals may be influenced more by exposure to each other than to the media. According to their theory, illustrated schematically in figure 1, a small minority of opinion leaders act as intermediaries between the mass media and the majority of society. Because information, and thereby influence “flows” from the media through opinion leaders to their respective followers, Katz and Lazarsfeld (1955) called their model the “two-step flow” of communication, in contrast with the then paradigmatic one-step, or “hypodermic,” model which treated individuals as atomized objects of media influence (Bineham 1988).
Insert figure 1 about here
In the decades after the introduction of the two-step flow, the idea of opinion leaders, or influentials as they are also called (Merton 1968), came to occupy a central place in the literatures of the diffusion of innovations (Coleman, Katz and Menzel 1966; Rogers 1995; Valente 1995), communications research (Weimann 1994), and marketing (Chan
and Misra 1990; Coulter, Feick and Price 2002; Myers and Robertson 1972; Van den Bulte and Joshi 2005; Vernette 2004). By the late 1960’s, the theory had been hailed as one of most important formulations in the behavioral sciences (Arndt 1967), and by the late 1970’s, according to Gitlin (1978), the two-step flow had become the “dominant paradigm” of media sociology. According to Weimann (1994), over 3,900 studies of influentials, opinion leaders, and personal influence were conducted in the decades after Katz and Lazarseld’s seminal Decatur study, leading Burt (1999, 38) to comment that the two-step flow had become “a guiding theme for diffusion and marketing research.” More recently still, Roch (2005, 110) has concluded that “In business and marketing, the idea that a small group of influential opinion leaders may accelerate or block the adoption of a product is central to a large number of studies.”
But what exactly does the two-step flow say about influentials, and how precisely do they exert influence over the (presumably much larger) population of non-influentials? In the remainder of this article we argue that although the dual concepts of personal influence and opinion leadership have been extensively documented, it is nevertheless unclear exactly how, or even if, the influentials of the two-step flow are responsible for diffusion processes, technology adoption, or other processes of social change. By simulating a series of formal models of diffusion that are grounded in the mathematical social science literature, we find that there are indeed conditions under which influentials are likely to be disproportionately responsible for triggering large-scale “cascades” of influence, and that under these conditions, the usual intuition regarding the importance of influentials is supported. These conditions, however, appear to be the exception rather than the rule—
under most conditions that we consider, influentials are only modestly more important than average individuals. In the models that we have studied, in fact, it is generally the case that most social change is driven not by influentials, but by easily influenced individuals influencing other easily influenced individuals. Based on these results, we argue that although our models are at best a simplified and partial representation of a complex reality, they nevertheless highlight that claims regarding the importance influentials should rest on carefully specified assumptions about who influences whom, and how.
THE INFLUENTIALS HYPOTHESIS
Katz and Lazarsfeld (1955) originally defined opinion leaders as “the individuals who were likely to influence other persons in their immediate environment,” and this definition remains in use, more or less unchanged (Grewal, Mehta and Kardes 2000, 236). It is important to note that opinion leaders are not “leaders” in the usual sense—they do not head formal organizations, nor are they public figures, such as newspaper columnists, critics, or media personalities, whose influence is exerted indirectly via organized media or authority structures. Rather, their influence is direct and derives from their informal status as individuals who are highly informed, respected, or simply “connected.” As Keller and Berry(2003) specify “It’s not about the first names that come to mind when you think about the people with influence in this country—the leaders of government, the CEO’s of larger corporations, or the wealthy. Rather, it’s
about millions of people…who shape the opinions and trends in our country.” Oprah Winfrey, in other words, may or may not influence public opinion in various ways—but that is a question primarily of media, not interpersonal, influence, and therefore is the subject of a different discussion.
Although the notion of opinion leadership seems clear, precisely how the influence of opinion leaders over their “immediate environment” shapes opinions and trends across entire communities, or even a country, is not specified by the two-step flow model itself. Often it is described simply as “a process of the moving of information from the media to opinion leaders, and influence moving from opinion leaders to their followers” (Burt 1999, 38), where the mechanics of the process itself are either left unspecified, or alternatively are asserted to derive from some diffusion process. Kelly et al. (1991, 168), for example, claim that “Diffusion of innovation theory posits that trends and innovations are often initiated by a relatively small segment of opinion leaders in the population,” and similar statements appear in fields as diverse as marketing (Chan and Misra 1990; Coulter et al. 2002; Van den Bulte and Joshi 2005), public health (Kelly et al. 1991; Moore et al. 2004, 189; O'Brien et al. 1999; Soumerai et al. 1998, p. 1358), and political behavior (Nisbet 2006; Roch 2005), as well as featuring in popular books (Barabasi 2002; Gladwell 2000) and commercial marketing publications (Burson-Marsteller 2001; Keller and Berry 2003; Rand 2004).
On closer inspection, however, the diffusion of innovations literature, does not specify any such theory. Rogers (1995, 281) does indeed state that “The behavior of opinion
leaders is important in determining the rate of adoption of an innovation in a system. In fact, the S-shape of the diffusion curve occurs because once opinion leaders adopt and tell others about the innovation, the number of adopters per unit time takes off.” But Rogers’ claim does not necessarily follow from the dynamics of diffusion processes—S-shaped diffusion curves do not, in fact, require opinion leaders at all. The “Bass model” (Bass 1969), for example, which is also popular in the marketing literature (Lehmann and Esteban-Bravo 2006), invariably generates S-shaped diffusion curves; yet it does so within an entirely homogenous population. Nor do other kinds of diffusion models (Bailey 1975; Dodds and Watts 2005; Dodson and Muller 1978; Granovetter 1978; Shrager, Hogg and Huberman 1987; Young 2006) require opinion leaders, or any kind of special individuals, in order to generate S-shaped diffusion curves, which are characteristic of virtually any self-limiting, contagious process.
The absence of special individuals in formal models of diffusion, of course, does not necessarily mean that they do not arise in real diffusion process, or that they do not play an important role. It does suggest, however, that the matter has not been resolved, either by the two-step flow itself, or by diffusion of innovations theory. To what extent, therefore, does the observation that some people are more influential than others in their immediate environment translate to the much stronger and more interesting claim that some special group of influentials plays a critical, or at least important, role in forming and directing public opinion? To address this question, we will study, using computer simulations, a series of mathematical models of interpersonal influence that invoke a variety of assumptions both about the nature of interpersonal influence and also about
influence networks. We emphasize that the models we consider, while plausible and somewhat general, are neither realistic nor exhaustive in their consideration of possible alternatives. However, we have consciously relaxed some of our more restrictive assumptions; thus to the extent that different assumptions consistently generate similar conclusions, these conclusions might be expected to have reasonably broad applicability.
A SIMPLE MODEL OF INTERPERSONAL INFLUENCE
We start by assuming that each individual i must make a decision with regard to some issue X. Following an extensive literature that encompasses sociology (Granovetter 1978), economics (Blume and Durlauf 2003; Brock and Durlauf 2001; Durlauf 2001; Schelling 1973; Young 1996), social psychology (Latané and L'Herrou 1996), political science (Kuran 1991), marketing (Goldenberg, Libai and Muller 2001; Mayzlin 2002), and mathematical physics (Borghesi and Bouchaud 2006; Watts 2002) we focus on binary decisions that exhibit “positive externalities,” meaning that the probability that individual i will choose alternative B over A will increase with the relative number of others choosing B. Positive externalities arise in a wide variety of areas of interest to marketing and diffusion researchers, including “network effects” (Liebowitz and Margolis 1998), coordination games (Latané and L'Herrou 1996; Oliver and Marwell 1985), social proof (Cialdini 2001), “learning from others” (Bikhchandani, Hirshleifer and Welch 1992; Salganik, Dodds and Watts 2006), and conformity pressures (Bernheim 1994; Bond and Smith 1996; Cialdini and Goldstein 2004). Furthermore, as Schelling
(1978), Granovetter (1978), and others (Banerjee 1992; Bikhchandani et al. 1992; Watts 2003) have argued, binary decisions can be applied to a surprisingly wide range of real-world situations. Thus while the class of binary decisions with positive externalities is not completely general—for example, it excludes cases in which individuals must choose simultaneously between many alternatives (De Vany and Walls 1996; Hedstrom 1998), and also excludes “anti-coordination” (Arthur 1994) or “snob” (Leibenstein 1950) behavior, for which negative externalities apply—it is an important and reasonably general case to consider.
Threshold Rule
Within this class, a widely studied decision rule, and the one we will consider initially (we will consider an alternative later) is called a “threshold rule,” which posits that individuals will switch from A to B only when sufficiently many others have adopted B in order for the perceived benefit of adopting a new innovation to outweigh the perceived cost (Lopez-Pintado and Watts 2006; Morris 2000; Schelling 1973). More formally, when b i , the fraction of i 's sample population that has adopted B is less than i 's
threshold !i , the probability that i will adopt B is zero, and when it exceeds !i , it jumps to one; that is,
P adopt B []=1if b i !"i ,0if b i <"i #$%
where individual differences with respect to subject matter expertise, strength of opinion, personality traits, media exposure, or perceived adoption costs, may correspond to heterogeneous thresholds (Valente 1995).
Influence Networks
In addition to describing a rule for how individuals influence each others’ decisions, we also need to specify who influences whom—that is, we require a formal description of the associated influence network. Unfortunately, empirical evidence regarding real-world influence networks is limited. Although a number of sociometric studies of influence have been conducted, beginning with Coleman et al.’s (1957) study of the diffusion of tetracycline, the resulting analyses have tended to focus on individual-level (Burt 1987) rather than network properties. More recently, a considerable amount of empirical work has been dedicated to measuring the properties of large-scale networks (Newman 2003; Watts 2004); however, only a handful of studies (Godes and Mayzlin 2004; Leskovec, Adamic and Huberman 2005) have attempted to study word-of-mouth influence empirically. These approaches, moreover, while promising, are not yet capable of measuring network structure in any detail. Finally, a small number of human subjects experiments (Kearns, Suri and Montfort 2006; Latané and L'Herrou 1996) have shed light on the relationship between of network structure and group coordination processes. However, the networks involved were artificially generated by the experimenters; thus they do not shed light on the structure of real-world influence networks.
In the absence of clear empirical evidence regarding the structure of influence networks, we assume that each individual i in a population of size N influences n
i
others, chosen
randomly, where n
i
is drawn from an influence distribution p n(), whose average n avg is
assumed to be much less than the size of the total population—that is, n
avg
!N. We
emphasize that although n
i
is mathematically equivalent to the notion of “acquaintance
volume” k
i , familiar to network analysts (Wasserman and Faust 1994), n
i
refers not to
how many others node i knows, but how many others i influences with respect to the particular issue, X, at hand. Thus n
i
should be thought of as some (possibly complicated) function not only of i's acquaintance volume, but also of i's personal characteristics, subject matter expertise, authority with respect to issue X, and even the characteristics of the other individuals in i's community (Katz and Lazarsfeld 1955; Rogers 1995).
The resulting influence network, illustrated schematically in figure 2, differs from the two-step flow schematic of figure 1 in two important ways. First, whereas in figure 1, influence can only flow from opinion leaders to followers, in figure 2, it can flow in either direction; and second, in figure 2 influence can propagate for many steps, whereas in figure 1 it can propagate only two. We note, however, that in both cases, figure 2 is consistent with available empirical evidence—arguably more so than figure 1. Numerous studies, including that of Katz and Lazarsfeld (1955), suggest that opinion leaders and followers alike are exposed to mixtures of interpersonal and media influence (Troldahl and Dam 1965), and that differences in influence are more appropriately described on a continuum than dichotomously (Lin 1973). Furthermore, while the implications of multi-
step flow for the influentials hypothesis have not been studied, multi-step flow itself has been recognized as a likely feature of most diffusion processes (Menzel and Katz 1955; Robinson 1976). Brown and Reingen (1987), for example, found that even in a relatively small population, 90% of recommendation chains extended over more than one step, and 38% involved at least four individuals.
Insert figure 2 about here
Although our model therefore embodies some important qualitative features of real-world interpersonal influence networks, it nevertheless contains two assumptions regarding the structure of these networks that merit critical examination in light of our objectives. First, the influence distribution p n(), which is necessarily Poisson (Solomonoff and Rapoport 1951), exhibits relatively little variation around its average. Influentials in such a world, that is, while clearly more influential than average, are rarely many times more influential. And second, aside from the distribution of influence, the network exhibits no other structure—it is entirely random. Although neither of these assumptions is demonstrably incorrect, neither is clearly correct either—the empirical evidence is unfortunately inconclusive—thus we will also present in a later section two variations of the basic model which relax both the homogeneity and the randomness assumptions.
Another advantage of formally defining an influence network, even with such a simple model, is that can now define more precisely what we mean by an “influential.” Previous empirical work has addressed the question of who should be considered influential, but a
clear answer remains elusive (Weimann 1991). Classical studies like those of Coleman et al. (1957) and Merton (1968) suggested that individuals who directly influence more than three or four of their peers should be considered influentials, while recent market-research studies have concluded that the number may be as high as fourteen (Burson-Marsteller 2001). Other studies, by contrast, define influentials in purely relative terms: Keller and Berry (Keller and Berry 2003), for example, define influentials as scoring in the top 10% of an opinion leadership test, while Coulter et al. (Coulter, Feick and Price 2002), using a similar test, treat the top 32% as influentials.
Here we follow the latter approach, and define an influential as an individual in the top q% of the influence distribution p n(). From a theoretical perspective, any particular value of q that we specify is necessarily arbitrary—indeed we have already argued that dichotomies such as that between opinion leaders and followers are neither theoretically derived nor empirically supported. Our purpose here, however, is not to defend any particular definition of influentials, but to examine the claim that influentials—defined in some reasonable, self-consistent manner—determine the outcome of diffusion processes. From this perspective, therefore, our definition has the advantage (over definitions that rely on absolute numbers) that it can be applied consistently to influence networks of
, and also different distributions p n(). In all results different average densities n
avg
presented here, we choose q=10%—a number that is consistent with previous studies (Keller and Berry 2003)—but we have also studied a wide range of values of q, and have determined that our conclusions do not depend sensitively on the specific choice.
Dynamics of Influence
Our model proceeds from an initial state in which all N individuals are inactive (state 0), with the exception of a single, randomly chosen initiator i, who is activated (state 1) exogenously. Depending on the model parameters and also the particular (randomly chosen) properties of i’s neighbors, this initial activation may or may not trigger some additional, endogenous activations. Subsequently, these newly activated neighbors may activate some of their own neighbors, who may in turn trigger more activations still, and so on, generating a sequence of activations, called a cascade (Watts 2002). When all activations associated with a single cascade have occurred, its size can be determined simply as the total cumulative number of activations. By repeating this process many times, where each time the population, the corresponding influence network, and the initial condition are all regenerated anew, it is possible to associate a distribution of cascade sizes with every choice of parameter settings. The implications for social contagion of different parameter values, and even different models, can then be assessed, either quantitatively or qualitatively, in terms of the properties of the corresponding cascade distributions.
Cascades of any size can, and do, occur in this model, but an important distinction in what follows is between “local” and “global” cascades. Local cascades affect only a relatively small number of individuals, and typically terminate within one or two steps of the initiator. The size of local cascades is therefore determined mostly by the size of an
initiator’s immediate circle of influence, not by the size of the network as a whole. Global cascades are the opposite—they affect many individuals, propagate for many steps, and are ultimately constrained only by the size of the population through which they pass. Importantly, global cascades can only occur when the influence network exhibits a “critical mass” of early adopters, which we define here as individuals who adopt after they are exposed to a single adopting neighbor. A critical mass can then be said to exist when sufficiently many early adopters are connected to each other that the network “percolates” throughout the entire influence network (Watts 2002). Although the critical mass may only occupy a small fraction of the total population, an interesting and subtle consequence of cascade dynamics is that once it is activated, the remainder of the population subsequently activates as well (Watts 2002), leading to a global cascade. But if the critical mass does not activate, or if it does not exist, then only local cascades are possible. In terms of the diffusion of innovations, the critical mass is therefore what enables an new idea or product to “cross the chasm” from innovation to success (Moore 1999).
IMPLICATIONS FOR THE INFLUENTIALS HYPOTHESIS
The most obvious way in which an individual can affect the size and likelihood of a cascade is by initiating one. Thus one way to quantify the relative importance of influentials is to compare the average size of a cascade initiated by an influential to that started by an average member of the population. Figure 3 makes this comparison explicit,
both in absolute (3A) and relative (3B) terms. Here we have chosen a value of the average activation threshold !=0.18 for which cascades are possible over some range of
the average density n
avg
of the influence network, but we have also studied many different choices of !, corresponding to innovations that are, on average, more of less appealing (or alternatively, populations that are more or less innovative). Although the particular choice of ! does affect the probability that a global cascade can take place, it affects influentials and non-influentials in a similar manner; thus our conclusions regarding the relative effect of influentials are largely independent of !. There are three points to emphasize from this figure, which we discuss in turn.
First, the size of cascades that can be triggered by single initiators varies enormously
depending on the average density n
avg of the influence network. When n
avg
is too low,
many individuals are highly susceptible to activation, but the network is too poorly connected for influence to propagate far, and thus activate more than a tiny fraction of the network. When n
avg
is too high, the opposite applies: the network is highly connected, but individuals now require multiple active neighbors to be activated themselves; thus small initial seeds are unable to grow. Only in the intermediate regime, called the “cascade window” (Watts 2002), can global cascades take place, and in that region both influentials and average individuals are likely to trigger them.
To a first approximation, therefore, the ability of any individual to trigger a cascade depends much more on the global structure of the influence network than on his or her personal degree of influence—that is, if the network permits global cascades, virtually
anyone can start one; and if it does not permit global cascades, nobody can. Once again, this statement does not depend on the particular choice of ! shown in figure 3, which is purely illustrative. If, for example, we chose some other value of ! that also lies within the region in which global cascades occur, the particular curves in figure 3A would shift, but the relationship between them, as shown in figure 3B, would remain qualitatively the same. Furthermore, if we were to choose a value of ! such that global cascades were not possible for any other model parameters—corresponding, say, to a highly resilient population, or an unappealing innovation—then the question of who triggers large cascades would obviously be moot: neither influentials nor non-influentials would be able to do so.
The second point to note, as illustrated in figure 3A, is that when large, multi-step cascades do occur, influentials (squares) do tend, on average, to trigger larger cascades (as well as more frequent large cascades) than average individuals (circles). Equivalently in figure 3B, the relative size of triggered cascades—what Van den Bulte and Joshi (2005) call the “multiplier effect” of influentials (squares)—is always at least one. Thus although it is clearly not the case that influentials are essential to a global cascade, it is also clear that they have more impact than average individuals. We note, however, that the relative impact of influentials is in most cases modest. For example, when n
avg
=3,
an influential directly influences more than twice as many people n
inf !6
() as an average individual, but multiplier effect (squares) barely exceeds one. Near the lower and upper boundaries of the cascade window, the multiplier effect does clearly exceed one, indicating that influentials are more effective in triggering cascades than ordinary
individuals. With the exception of a narrow interval (shaded vertical strip) near the upper boundary, however, the relative multiplier effect (dashed line)—the ratio of the absolute
multiplier for influentials to their relative direct influence n
inf n
avg
(solid line)—is less
than one, meaning that the size of the cascades triggered by influentials, although larger than average, is less than proportional to the number of individuals they influence directly.
Insert figure 3 about here
Taken together, our results provide mixed support for what we have called the influentials hypothesis. The strongest version of the hypothesis—that influentials are in some way essential to diffusion—is clearly not supported, as influentials are neither necessary nor sufficient to trigger large cascades. But the issue of whether or not influentials satisfy some lesser criterion of “importance” beyond their immediate neighborhoods is harder to resolve. Certainly they tend to trigger larger cascades than average, but whether they trigger sufficiently larger cascades to qualify as “important” is a matter of debate. Sometimes, as in the shaded region of figure 3B, the impact of influentials is not only greater than average, but disproportionately great—a criterion that would seem to serve as a reasonable definition of important. But if that is so, then figure 3 also shows that under most conditions, influentials are not so important—their global impact is almost always less than proportional to their influence measured locally, and often it is only marginally greater than average.
Importance of course, may be irrelevant. It may be sufficient that influentials are simply more effective than average, even if only marginally so. Given the intense interest that influentials and opinion leaders have attracted ever since they were first hypothesized over 50 years ago, it seems unlikely that marginal importance is all that is being asserted on their behalf. Influentials, by definition, are relatively rare—here they constitute just 10% of the population—thus they are necessarily more difficult to locate than average individuals, and possibly more difficult to mobilize also (although that is not necessarily the case). Whether or not the additional impact of influentials justifies paying special attention to them—versus, for example, focusing on some other group, or even recruiting individuals at random—is therefore a matter that will depend, possibly delicately, on the various costs associated with different strategies, and the particular details of the influence network.
A second way, however, in which influentials may play a critical role in driving large cascades is as early adopters, who in our model comprise the critical mass via which local cascades become global. Restricting our attention exclusively to “global” cascades (which in our simulations means cascades that reach more than 1,000 individuals in a population of 10,000) we now consider the extent to which influentials (defined still as the top 10% of the influence distribution) appear as early adopters. Figure 4 shows that when influence networks are sparse (near the lower boundary of the cascade window) early adopters tend to be more influential than average (4A), but when influence networks are dense (i.e. at the upper boundary of the cascade window), the opposite result obtains—early adopters are substantially less influential than average (4B).
That early adopters should be at times more influential than average, and at other times less so has been noted previously (Boorman and Levitt 1980; Rogers 1995). However, the origin of the result here is somewhat different from the usual intuition that “central” individuals are highly attuned to group norms; thus will tend to adopt early when the group as a whole is progressive, and later when the group is conservative. In our model, no awareness of group norms is necessary or indeed possible. Rather, the principal criterion for an individual to be an early adopter is simply whether or not they can be activated by a single active neighbor. Because the threshold required for this “vulnerability” is inversely proportional to how influential an individual is (Watts 2002), then on average, highly influential individuals are less likely to be vulnerable (we will reverse this assumption in the next section). On the other hand, however, the more influential an individual, the more others they can potentially activate once they themselves are activated. To spread globally, therefore, cascades must strike a compromise, propagating via the most influential of the most easily influenced individuals.
Insert figure 4 about here
is low, almost everyone is easy to
A consequence of this tradeoff is that when n
avg
influence and influential nodes are therefore preferred; but when n
is high, only below-
avg
average nodes are sufficiently vulnerable to be activated early on. As figure 4 indicates, however, even when the early adopters are more influential than average, they still do not
tend to be influentials. Specifically, we find that regardless of the density of the influence networks, the average influence of the top 10% of the influence distribution clearly exceeds that of the early adopters, as indicated by the dashed horizontal lines in figures 4A and 4B. At least to the extent that the essential features of influence processes are captured by this model, therefore, large scale changes in public opinion are not driven by highly influential people who influence everyone else, but rather by easily influenced people, influencing other easily influenced people.
VARIATIONS ON THE BASIC INFLUENCE MODEL
Obviously these conclusions, although qualitative in nature, are derived from our analysis of a particular formal model—a model which is, after all, exceedingly simple, and which makes a number of important assumptions. One might suspect, therefore, that if the assumptions of the model were altered in certain respects, our conclusions regarding the importance of influenitals would change as well. To address this concern, we now introduce a series of related models, which invoke different assumptions both with regard to interpersonal influence and also influence networks. It will come as no surprise that these different models can generate at times dramatically different overall dynamics of influence cascades, both quantitatively and qualitatively. We will also find some additional conditions under which influentials do appear to play important roles, either as initiators or as early adopters. Nevertheless, our conclusion that under most conditions cascades are driven by easily influenced, not highly influential, individuals will remain.
法律基础知识讲义 一、法律关系 1、法律关系：法律调整在人们行为的过程中形成的权利、义务关系，即依据法律的规定人们应该做什么和不应该做什么。 2、权利：是指公民依法享有的权益，它表现为享有权利的公民有权做出一定的行为和要求他人做出相应的行为。 3、义务：是指公民依法应当履行的职责，它表现为负有义务的公民必须做出一定的行为或禁止做出一定的行为。 二、合同、侵权 1、合同：平等主体的自然人、法人、其他组织之间设立、变更、终止民事权利义务关系的协议。依法成立的合同，受法律保护，双方当事人必须依法履行合同，否则要承担违约责任。合同可以是书面的也可以是口头的。“合同”同“协议”。 合同法律关系是我们日常生活中最常见的民事法律关系。每个人几乎每天都会与别人签订各种各样的合同，建立合同法律关系。大家早上起来买早餐便与餐厅之间建立了买卖合同法律关系，若餐厅出售了变质的食物，即构成了违约责任，我们便可以追究其违约责任，要求退还款项、赔偿损失；乘车与公交公司建立运输合同关系，租房与出租人建立租赁合同关系。因此，合同无处不在，合同纠纷自然成为人们社会活动中最常见的纠纷。 2、侵权：是指行为人故意或过失侵害他人财产或者人身,造成他人损害,依法应当承担民事责任的行为。只要是因故意或过失给他人造成了损害，受害人就可以要求侵害人赔偿损失。 侵权纠纷也是我们日常生活中最常见的纠纷类型之一。如侵犯名誉权（恶意造谣，毁坏他人名誉）、肖像权（未经许可将他人肖像利用在商业广告中）、侵犯公司名称权、侵犯健康权、侵犯财产权等等。 三、常见纠纷 1、民事合同纠纷：买卖合同、借款合同、租赁合同、代理合同、合作合同
一、对等网简介 “对等网”也称“工作组网”，那是因为它不像企业专业网络中那样是通过域来控制的，在对等网中没有“域”，只有“工作组”，这一点要首先清楚。正因如此，我们在后面的具体网络配置中，就没有域的配置，而需配置工作组。很显然，“工作组”的概念远没有“域”那么广，所以对等网所能随的用户数也是非常有限的。在对等网络中，计算机的数量通常不会超过20台，所以对等网络相对比较简单。在对等网络中，对等网上各台计算机的有相同的功能，无主从之分，网上任意节点计算机既可以作为网络服务器，为其它计算机提供资源；也可以作为工作站，以分享其它服务器的资源；任一台计算机均可同时兼作服务器和工作站，也可只作其中之一。同时，对等网除了共享文件之外，还可以共享打印机，对等网上的打印机可被网络上的任一节点使用，如同使用本地打印机一样方便。因为对等网不需要专门的服务器来做网络支持，也不需要其他组件来提高网络的性能，因而对等网络的价格相对要便宜很多。 对等网主要有如下特点： （1）网络用户较少，一般在20台计算机以内，适合人员少，应用网络较多的中小企业； （2）网络用户都处于同一区域中； （3）对于网络来说，网络安全不是最重要的问题。 它的主要优点有：网络成本低、网络配置和维护简单。 它的缺点也相当明显的，主要有：网络性能较低、数据保密性差、文件管理分散、计算机资源占用大。 二、对等网结构 虽然对等网结构比较简单，但根据具体的应用环境和需求，对等网也因其规模和传输介质类型的不同，其实现的方式也有多种，下面分别介绍： 1、两台机的对等网 这种对等网的组建方式比较多，在传输介质方面既可以采用双绞线，也可以使用同轴电缆，还可采用串、并行电缆。所需网络设备只需相应的网线或电缆和网卡，如果采用串、并行电缆还可省去网卡的投资，直接用串、并行电缆连接两台机即可，显然这是一种最廉价的对等网组建方式。这种方式中的“串／并行电缆”俗称“零调制解调器”，所以这种方式也称为“远程通信”领域。但这种采用串、并行电缆连接的网络的传输速率非常低，并且串、并行电缆制作比较麻烦，在网卡如此便宜的今天这种对等网连接方式比较少用。 2、三台机的对等网
合规管理基础知识 第一章《合规与银行合规部门》简介2003年10月，巴塞尔银行监督管理委员会（以下简称“巴塞尔委员会）就银行合规问题发布了题为《银行的合规职能》（《The compliance function in bank》）的文件,在世界围广泛征求各方面意见。经过历时一年半的征求意见和反复修改，巴塞尔委员会终于在2005年5月正式发布了《合规与银行合规职能》（《Compliance and the compliance function in banks》）（以下简称“《文件》”）。《文件》首先肯定了合规风险管理已经作为一项日趋重要并且高度独立的风险管理职能而存在，其次界定了合规管理的涵，然后明确了董事会和高级管理层的合规职责，最后阐述了关于银行部合规职能的组织、结构、作用以及其他相关问题。 早在《文件》出台之前，以汇丰银行和花旗银行为代表的国际化大银行就已经开始创建其合规工作机制，至今已经颇具规模。在世界围，越来越多的银行认识到合规的重要性并不同程度地开展了合规工作。《文件》既是对于国际领先银行合规工作的经验总结，为世界各地银行提供了银行合规工作的基本指引；同时也明确了国际银行监管标准的发展趋势，拓宽了银行监管当局在银行是否合规方面的监管视野。
第二章合规管理的基本涵 对于大多数国商业银行而言，尽管“依法合规”一直是其经营理念之一，而且事实上已经开展了合规工作，但是“合规”相对于银行其他业务仍处在初级阶段。面对监管机构逐渐加大合规监管工作的力度以及日益复杂的国际金融形势，国银行如何拓展合规工作的广度和深度显得日趋紧迫。 1998年9月，早在《文件》发布之前的5年，巴塞尔委员会在《银行业组织部控制体系框架》中将“合法和合规性，即合法方面的目标”列为银行业组织部控制体系框架的三个目标之一。合法合规性目标要求所有的银行业务应当与法律、法规、监管要求、银行政策和程序相符合，以保护银行的权利和声誉。2005年5月发布的《文件》可以视作是对于合法合规目标的具体阐述，其将合规进一步明确界定为银行部应当具有的一种独立职能，其目的在于管理银行自身的合规风险。为了对于合规管理有全面客观的的认识，我们可以结合《文件》，从合规管理是什么、合规管理由谁做以及合规管理如何做这三个方面来认识合规管理的基本涵。 2.1合规管理是什么？ 合规管理是银行部所应具有的管理自身合规风险的独立职—2 —
汇博通文档借阅管理系统使用说明书 汇博通知识管理系统的属性管理，实际上已提供了借阅与归还功能，但那是针对每一份文件 或档案而言的。 这里，为客户提供一款专门用于文档的借阅与归还的软件，不但可办理一份文件的借阅或归 还手续，只要有需要，也可批量办理借阅与归还，另外，还提供了与借阅有关的一系列统计 报表。 发放功能与借阅类似，所不同的只是发放不必归还，如将购买的资料、图书发放给职员学习 等。 注：借阅与归还模块的操作，需要获得以下三种权限中的一种： 系统管理员 归档授权（档案管理员） 编号授权（文件管理员） 与借阅与归还模块相关的系统参数的设置说明如下：
首页 汇博通主页的模块工具条上，有一个借阅与归还的按钮，单击它即进入借阅与归还首页。 借阅（发放） 前面已经介绍过，借阅与发放的区别在于，借阅需要归还，发放则不必归还，从某种意义上 来说，发放实际上已将所有权（或有条件的所有权）转移给接收者。 借阅界面包括左右两个子窗体，左侧子窗体用于显示可供借阅（发放）的文档，其上部有搜 索关键词输入框，输入相应关键词即可查找出可供借阅的相应文档，如果要借阅的文档已经 在操作者手上，并且，标注有条形码或电子标签，操作者可直接通过条码阅读器或电子标签
阅读器读取相应编码直接获取到该文档。 根据实际需要，通过点选左侧的复选框，选择具体文档，然后，通过点击两个子窗体中间的箭头，即可将选中的文档添加到右侧子窗体的列表中，即可直接办理借阅或发放手续。 可供借阅（发放）检索列表待选区。借阅（发放）选择勾选列表区。 可供借阅（发放） 输入文件名称、编号、责任者或主题词等属性，点击【检索】按钮进行查找，如下图： 勾选确定后点击该按钮，即可添加到已 选择列表区中。
范文 2020年公务员考试法律基础知识复习讲义：著作权 1/ 6
法 著作权法◆ 不适用本法保护的对象第五条本法不适用于：（一）法律、法规，国家机关的决议、决定、命令和其他具有立法、行政、司法性质的文件，及其官方正式译文；（二）时事新闻；（三）历法、通用数表、通用表格和公式。 ◆ 著作权的归属第九条著作权人包括：（一）作者；（二）其他依照本法享有著作权的公民、法人或者其他组织。 第十一条著作权属于作者，本法另有规定的除外。 创作作品的公民是作者。 由法人或者其他组织主持，代表法人或者其他组织意志创作，并由法人或者其他组织承担责任的作品，法人或者其他组织视为作者。 如无相反证明，在作品上署名的公民、法人或者其他组织为作者。 第十五条电影作品和以类似摄制电影的方法创作的作品的著作权由制片者享有，但编剧、导演、摄影、作词、作曲等作者享有署名权，并有权按照与制片者签订的合同获得报酬。 电影作品和以类似摄制电影的方法创作的作品中的剧本、音乐等可以单独使用的作品的作者有权单独行使其著作权。 第十六条公民为完成法人或者其他组织工作任务所创作的作品是职务作品，除本条第二款的规定以外，著作权由作者享有，但法人或者其他组织有权在其业务范围内优先使用。 作品完成两年内，未经单位同意，作者不得许可第三人以与单位使用的相同方式使用该作品。
有下列情形之一的职务作品，作者享有署名权，著作权的其他权利由法人或者其他组织享有，法人或者其他组织可以给予作者奖励：（一）主要是利用法人或者其他组织的物质技术条件创作并由法人或者其他组织承担责任的工程设计图、产品设计图、地图、计算机软件等职务作品；（二）法律、行政法规规定或者合同约定著作权由法人或者其他组织享有的职务作品。 ◆ 权利的保护期 3/ 6
第一章金融市场体系 第一节全球金融体系 一．金融市场的概念 1．金融市场以金融资产为交易对象，是一种交易机制及其关系的总和，以金融资产的供给方和需求方为交易主体。 广义定义：实现货币接待和资金融通，办理各种票据和有价证券交易活动的市场。完善定义：金融市场是交易金融资产并确定金融资产价格的一种机制。 二．金融市场的分类 1.按交易标的物划分：考试最常见 货币市场：同业拆借市场，票据市场，回购市场，货币基金（MMF）资本市场：股票，债券，基金 外汇市场 金融衍生品市场：特征：杠杆，信用交易（期货，期权，互换，远期）保险市场 黄金市场：伦敦，苏黎世，纽约，香港 2.按交易对象是否新发行 一级市场（又称发行市场，初级市场）
二级市场（又称流通市场，次级市场）：分为场内市场（证券交易所市场，集中交易市场），场外市场（柜台交易市场，店头交易市场） 3.根据融资方式划分：直接融资，间接融资 4.根据地域范围划分：国际金融市场，国内金融市场 5.根据经营场所划分：有型金融市场，无型金融市场 6.根据交割期限划分：金融现货市场，金融期货市场 7.根据交易对象的交割方式划分：即期交易市场，远期交易市场 8.按交易对象是否依赖其他金融工具：原生金融市场，衍生金融市场 9.按价格形成机制划分：竞价市场，议价市场 三．影响金融市场主要因素 经济因素，法律因素，市场因素，技术因素 四．金融市场的特点 1.交易对象：货币，资金，其他金融工具 2.价格一致性，利率在市场机制作用下趋向一致 3.有形，无形市场均可交易 4.自由竞争市场 5.非物质化
6.交易既体现了买卖关系，也是借贷关系提现资金所有权和使用权 7.现代金融市场是信息市场 五．金融市场的功能 资本累积：聚敛功能 资源配置：资源配置，财富再分配，风险再分配 调节经济：直接调节，间接调节 反映经济：反映微观经济，反映货币供应量，反映企业经营，金融市场一体化 六．非证券金融市场 股权投资市场，信托市场，融资租赁市场 七．全球金融市场的形成及发展趋势 一体化，证券化，金融创新 八．国际资金流动方式 按资本的使用期限长短划分 1. 长期资本流动 国际直接投资（FDI）：创独资企，合营企业，收购，股票股权，利润再投资
5S管理系列讲义之二 目视管理 一、目视管理概述 1、目视管理的含义 目视管理是通过视觉导致人的意识变化的一种管理方法。据统计，人的行动的60%是从“视觉”的感知开始的，因此在管理中，通过目视管理使各种管理状态和方法“一目了然”，使员工通过眼睛的观察就能把握现场运行状况，让员工能及时、准确地判断问题，达到“自主管理”的目的。因为没有信息就不可能发挥出主观能动性，要保证获取信息，最好的方法就是通过简单的直观管理技木——目视管理。 2、目视管理的作用 1)暴露异常及问题； 2)使员工了解应管理控制的项目； 3)创造高效率的工作环境。 3、目视管理评价 ①目视管理要符合以下要求： 1)无论谁都能判明异常和好坏； 2)能迅速判断，且判断的准确程度高； 3)判断结果不会因人而异。 ②目视管理水平可分为3个级别： 初级水平：有表示，能明白现在的状况； 中级水平：谁都能判断当前的状态和问题； 高级水平：列明管理方法〔如异常处置办法等〕。
3、目视管理对象
①车间目视管理 车间生产现场的对象包括质量、交货期、成本、安全、5S。其具体内容是： 1)作业管理 ●目视作业标准：如利用照片、图片做成的标准书； ●颜色管理：：如工具、零件定置场所的色彩管理； ●限度样本； ●异常警示灯或蜂鸣器； ●标示、看板； ●区域线：如不良品、半成品定置而所的标示； ●人员配置图； ●技能地图：如作业熟练程度的表示； ●安全标志：危险区域的标志。 2)交期管理 ●生产进度管理板； ●生产计划表； ●派工板。 3)品质管理 ●检查工具、夹具的层别管理； ●不良品层别管理； ●量具仪器梭正色别管理：如检测器具精度在规定值内和外的颜色区别； ●直方图； ●检查指导书。 4)设备管理 ●加油色别管理：如加油口的颜色标示； ●操作动作的顺序指引； ●保养部位色别管理：如定期保养部位的颜色标示； ●危险动作部位色别管理：如紧急停止开关的颜色标示； ●换模部位与固定部件的颜色区分； ●仪表安全法围色别管理：如控制范围内、外的颜色区分； ●螺丝、螺栓的配合记号； ●管路色别管理：如对相似的油或溶剂的颜色区分。 ②办公室目标管理 办公室目视管理主要是信息的共有化、业务的标准化和简单化，以迅速、正确地为生产现场提供信息，并有效解决问题。具体的内容是： 1)文件管理 ●文件的分类标示； ●文件保管场所的标示； ●文件的定位标示。 2)行动管理 ●人员动态管理图板； ●个人工作计划； ●出勤管理 3)业务管理 ●业务标准手册
XX集团—人力资源管理系统操作手册 目录 常用操作（新人必读） (2) 1.基础数据管理 ................................................................................................................... - 5 - 1.1组织架构 (5) 1.2职位体系 (8) 1.3职员维护 (11) 1.4结束初始化.................................................................................. 错误！未定义书签。 2.组织管理业务 ................................................................................................................. - 27 - 2.1组织规划 (27) 2.2人力规划 (33) 2.3组织报表 (38) 3.员工管理业务 ................................................................................................................. - 41 - 3.1员工状态管理 (41) 3.2合同管理 (41) 3.3后备人才管理 .............................................................................. 错误！未定义书签。 3.4人事事务 (52) 3.5人事报表 (59) 4.薪酬管理 ......................................................................................................................... - 69 - 4.1基础数据准备 (69) 4.2薪酬管理日常业务 (92) 4.3薪酬管理期末业务 (107) 4.4薪酬报表 (108)
银行校园招聘考试 金融部分基础知识讲义和总结 第一部分：国际货币与国际金融 一、不同货币制度下汇率是如何决定的？ 在金本位制下,两国货币之间的汇率是由两国本位币的含金量之比,即铸币平价决定的。在纸币流通条件下,两国货币间的比价，是由两国货币在外汇市场上的供求状况来决定的。 二、在纸币流通条件下,影响各国汇率变动的深层因素有哪些? 在纸币流通条件下，外汇供求状况对汇率变动的影响只是表面的，汇率变动还有更深层次的影响因素： ．国际收支。国际收支逆差,对外债务增加,对外汇需求增加,外汇汇率上升,本币汇率下降。 国际收支顺差,对外债权增加,对本币需求增加,本币汇率上升，外汇汇率下降。 ．通货膨胀。通货膨胀，纸币贬值，引起国际收支亏损，贬值货币也会在市场上遭到抛售，造成本币汇率下降。 ．利率。利率的高低调整会使外资的流入或流出增加,改变外汇的供求状况,引起汇率的变动。 .经济增长状况。经济增长在短期内会带来更多的进口,造成经常项目逆差，本币汇率下降。 长期看，一国经济实力的增强会增强对其货币的信心,使其币值稳定坚挺。 ．中央银行的干预。中央银行出于某种经济政策的考虑，可能会对外汇市场进行直接干预，在短期内会影响外汇市场的供求状况和汇率的变动。 .市场预期。在国际金融市场上，庞大的游资流动迅速，受预期因素的影响很大,预期因素成为短期内影响汇率变动的最主要因素之一。 三、造成国际收支失衡的原因是什么？ 造成国际收支失衡的原因主要有： .经济发展的不平衡。在各国的经济增长过程中,由于经济制度、经济政策等原因会印发周期性经济波动，导致国际收支恶化。这属于周期性失衡。 ．经济结构性原因。一国的产业结构与国际分工结构失调,无法适应国际市场的需求，会引起该国的国际收支的失衡。这属于结构性失衡。 .货币价值的变动。当一国出现通货膨胀，物价上涨，短期内出口商品的价格没有竞争力，导致国际收支恶化，当一国出现通货紧缩,物价下降，短期内出口商品具有价格优势,国际收支顺差。这属于货币性失衡。 国民收入的变动。当一国的国内和国外的政治和经济发生重大变化，引起国民收入的变动，也成为影响国际收支不平衡的重要因素。 四、如何对国际收支失衡进行调节？ 当国际收支失衡时,可以采用以下的一些方法进行调节: .商品调节。采取对出口商品给予补贴用优惠利率刺激出口等奖出限入的措施来改善国际收支逆差。 实施金融政策。以调节利率或汇率的办法来平衡国际收支。当国际收支出现逆差,就利用
物联网技术与应用 对等网络配置及网络资源共享 实验报告 组员：
1.实验目的 （1）了解对等网络基本配置中包含的协议，服务和基本参数 （2）了解所在系统网络组件的安装和卸载方法 （3）学习所在系统共享目录的设置和使用方法 （4）学习安装远程打印机的方法 2.实验环境 Window8，局域网 3.实验内容 （1）查看所在机器的主机名称和网络参数，了解网络基本配置中包含的协议，服务和基本参数 （2）网络组件的安装和卸载方法 （3）设置和停止共享目录 （4）安装网络打印机 4.实验步骤 首先建立局域网络，使网络内有两台电脑 （1）“我的电脑”→“属性”，查看主机名，得知两台计算机主机名为“idea-pc”和“迦尴专属”。 打开运行输入cmd，进入窗口输入ipconfig得到相关网络参数。局域网使用的是无线局域网。 （2）网络组件的安装和卸载方法：“网络和共享中心”→“本地连接”→“属
性”即可看到网络组件，可看其描述或卸载。 “控制面板”→“卸载程序”→“启用和关闭windows功能”，找到internet 信息服务，即可启用或关闭网络功能。 （3）设置和停止共享目录（由于windows版本升高，加强了安全措施和各种权
限，所以操作增加很多） 使用电脑“idea-pc”。“打开网络和共享中心”→“更改高级选项设置”。将专用网络，来宾或公用，所有网络中均选择启用文件夹共享选项，最下面的密码保护项选择关闭，以方便实验。 分享文件夹“第一小组实验八”，“右键文件夹属性”→“共享”→“共享”，选择四个中的一个并添加，此处选择everyone，即所有局域网内人均可以共享。
前言 本《操作手册》内容是按该软件主界面上第一横排从左至右的顺序对各个功能加以介绍的，建议初学者先对第一章系统设置作初步了解，从第二章基础资料读起，回头再读第一章。该管理软件的重点与难点是第二章，望读者详读。 第一章系统设置 打开此管理软件，在主界面上的左上方第一栏就是【系统设置】，如下图所示： 点击【系统设置】，在系统设置下方会显示【系统设置】的内容，包括操作员管理、数据初始化、修改我的登录密码、切换用户、选项设置、单据报表设置、导入数据、数据库备份、数据库恢复、压缩和修复数据库、退出程序。下面分别将这些功能作简要介绍： 1.1操作员管理 新建、删除使用本软件的操作员，授权他们可以使用哪些功能。此功能只有系统管理员可以使用。 1.1.1 进入界面 单击【系统设置】，选择其中的【操作员管理】，画面如下：
1.1.2、增加操作员 单击【新建】按钮，画面如下： 输入用户名称、初始密码、选择用户权限，可对用户进行适当描述，按【保存】后就点【退出】，就完成了新操作员的添加，效果如下图。
1.1.3 删除操作员 选择要删除的操作员，单击【删除】按钮。 1.1.4 修改操作员 选择要修改的操作员，单击【修改】按钮，可对操作员作相应修改，修改后需保存。 1.1.5 用户操作权限 选择要修改的操作员，单击【修改】按钮，出现以下画面，点击【用户权限】栏下的编辑框，出现对号后点【保存】，该操作员就有了此权限。 1.2数据初始化 1.2.1进入界面 单击【系统设置】，选择其中的【数据初始化】，画面如下：
1.2.2数据清除 选择要清除的数据，即数据前出现对号，按【确定】后点【退出】，就可清除相应数据。 1.3 修改我的登录密码 1.3.1进入界面 单击【系统设置】，选择其中的【修改我的登录密码】，画面如下： 1.3.2密码修改 输入原密码、现密码，然后对新密码进行验证，按【确定】后关闭此窗口，就可完成密码修改。 1.4 切换用户 1.4.1进入界面 单击【系统设置】，选择其中的【切换用户】，画面如下：
法律基础知识专题讲座讲稿 <1>大家好！首先，自我介绍（我是乌鲁木齐鼎信旭业律师事务所的律师，也是该所的合伙人之一··）。作为贵公司的法律顾问，能有这样的机会和大家一起学习一些基本的法律知识，我感到很荣幸，希望大家能与我一同轻松愉快地度过接下来的两个多小时。 <2>咱们言归正传，先来看一些今天讲座会涉及到那些方面的法律知识。今天，咱们要讨论九个方面的内容，首先是法学理论，其次是民法，民事诉讼法，刑法，还有劳动合同法，大家看我的这个表格，在民法下面，还有几个分支，当然民法并不是仅仅包括这几个方面，我只是选取了其中一些我认为在大家的日常生活中比较常见的来和大家一起学习。可能会有人有疑问，在我的表格里，合同法是属于民法的，那劳动合同法不也是讲合同吗？为什么就不属于民法呢？对于这个问题，在接下来的法学理论中就能找到答案。那么下面咱们就第法学理论的学习吧。 <3>在法学理论这一节中，咱们会见到法律部门，法律体系，社会主义法系，中国现代法制化进程这样几个词语。我给大家一一介绍。
<4>法律部门，又称为部门法，是运用特殊调整方法调整一定种类社会关系的法律规范的总和。在现行法律规范中，由于调整的社会关系及其调整方法不同，可分为不同的法律部门，凡调整同一类社会关系的法律规范的总和，就构成一个独立的法律部门。 部门法所指的同类法律，不包括国际法，如国际公法、国际私法和国际经济法等，它仅指国内法。不包括已经失效的法，它仅指现行法；也不包括将要制定但尚未制定的法律，它仅指已经颁布生效的法律。我国的法律体系大体上分这几个门类：宪法及宪法相关法、民商法、行政法、经济法、社会法、刑法、诉讼与非诉讼程序法这七个主要的法律部门。 部门法或法律部门具有自己的特征。首先，一个法律体系的所有部门法是统一的，各个部门法之间是协调的。其次，各个部门法又是相对独立的，因为它们各自所调整的社会关系不同，每一个法律部门都调整一定的社会现象所反映的社会关系。如民法调整平等主体之间的人身和财产关系；刑法调整国家社会的统治秩序和社会规范与犯罪行为之间的社会关系；商法调整的是市场经济下商主体和商行为之间的关系；民事诉讼法调整民事诉讼活动和民事诉讼关系的法律规范的；调整劳动关系的法律规范的总和构成劳动法部门等等；第三，法律部门是基本确定的，又是不断变化的，在一法律部门确定后，会持续保持相对一段时间，同时随着社会的发
对等网络(P2P 一、概述 (一定义 对等网络(P2P网络是分布式系统和计算机网络相结合的产物,在应用领域和学术界获得了广泛的重视和成功,被称为“改变Internet的新一代网络技术”。 对等网络(P2P:Peer to Peer。peer指网络结点在: 1行为上是自由的—任意加入、退出,不受其它结点限制,匿名; 2功能上是平等的—不管实际能力的差异; 3连接上是互联的—直接/间接,任两结点可建立逻辑链接,对应物理网上的一条IP路径。 (二P2P网络的优势 1、充分利用网络带宽 P2P不通过服务器进行信息交换,无服务器瓶颈,无单点失效,充分利用网络带宽,如BT下载多个文件,可接近实际最大带宽,HTTP及FTP很少有这样的效果 2、提高网络工作效率 结构化P2P有严格拓扑结构,基于DHT,将网络结点、数据对象高效均匀地映射到覆盖网中,路由效率高 3、开发了每个网络结点的潜力 结点资源是指计算能力及存储容量,个人计算机并非永久联网,是临时性的动态结点,称为“网络边缘结点”。P2P使内容“位于中心”转变为“位于边缘”,计算模式由“服务器集中计算”转变为“分布式协同计算”。
4、具有高可扩展性(scalability 当网络结点总数增加时,可进行可扩展性衡量。P2P网络中,结点间分摊通信开销,无需增加设备,路由跳数增量小。 5、良好的容错性 主要体现在:冗余方法、周期性检测、结点自适应状态维护。 二、第一代混合式P2P网络 (一主要代表 混合式P2P网络,它是C/S和P2P两种模式的混合;有两个主要代表: 1、Napster——P2P网络的先驱 2、BitTorrent——分片优化的新一代混合式P2P网络 (二第一代P2P网络的特点 1、拓扑结构 1混合式(C/S+P2P 2星型拓扑结构,以服务器为核心 2、查询与路由 1用户向服务器发出查询请求,服务器返回文件索引 2用户根据索引与其它用户进行数据传输 3路由跳数为O(1,即常数跳 3、容错性:取决于服务器的故障概率(实际网络中,由于成本原因,可用性较低。
2020年国家公务员考试《公务员法》复习讲义（重 点版）含真题 从历年真题来看，每年涉及公务员法的内容均有一至两分，而且不管是否涉及，对于每个有志于跨入公务员队伍的考生来说，公务员法都是必需要掌握的内容。 【重点法条】 第二条本法所称公务员，是指依法履行公职、纳入国家行政编制、由国家财政负担工资福利的工作人员。 第三条公务员的义务、权利和管理，适用本法。 法律对公务员中的领导成员的产生、任免、监督以及法官、检察官等的义务、权利和管理另有规定的，从其规定。 相关法条： 第一百零五条本法所称领导成员，是指机关的领导人员，不包括机关内设机构担任领导职务的人员。 第一百零六条法律、法规授权的具有公共事务管理职能的事业单位中除工勤人员以外的工作人员，经批准参照本法进行管理。 【意思分解】 公务员法所称公务员不仅限于行政机关中的公务员，还包括权力机关、审判机关、检察机关中的公务员，甚至包括中国共产党员及民主党派的机关工作人员、政协机关工作人员。军事机关工作人员不属于《公务员法》意义上的公务员。 【真题回顾】 （2008年上海真题） 99、《公务员法》对公务员的范围重新做了界定，根据规定公务员必须具备的三个条件中属于公务员最本质的特征是（A ） A 依法履行公职 B 纳入国家行政编制 C 由国家财政负担工资福利 D 公务员的职务行为受法律保护 【重点法条】 第十一条公务员应当具备下列条件： （一）具有中华人民共和国国籍；
（二）年满十八周岁； …… （七）法律规定的其他条件。 第二十四条下列人员不得录用为公务员： （一）曾因犯罪受过刑事处罚的； （二）曾被开除公职的； （三）有法律规定不得录用为公务员的其他情形的。 【快捷记忆】 国籍八宪品身化，不录开除受刑罚。 【快捷记忆】 （2008年江苏真题C类） 29、根据《公务员》的规定，不得录用为公务员的情形是（C D ）。 A 曾因违纪受过处分的 B 曾因个人原因辞去公职 C 曾因犯罪受过刑事处罚的 D 曾被开除公职的 第三十二条新录用的公务员试用期为一年。试用期满合格的，予以任职；不合格的，取消录用。 【意思分解】掌握公务员任职的基本条件，说明一下，这是一般条件，《法官法》《检察官法》规定的条件较高些。特别要注意不得录用公务员的几类人员。另外公务员的试用期为1年，与《劳动法》规定的试用期期限不同。 【重点法条】 第二十二条中央机关及其直属机构公务员的录用，由中央公务员主管部门负责组织。地方各级机关公务员的录用，由省级公务员主管部门负责组织，必要时省级公务员主管部门可以授权设区的市级公务员主管部门组织。 【意思分解】了解一下，什么组织的公务员主管部门可以招录。（省级以上） 【重点法条】 第三十八条公务员职务实行选任制和委任制。 领导成员职务按照国家规定实行任期制。 第三十九条选任制公务员在选举结果生效时即任当选职务；任期届满不再连任，或者任期内辞职、被罢免、被撤职的，其所任职务即终止。
对等网络的网络弹性分析 摘要：网络弹性研究的是网络在节点失效或被有意攻击下所表现出来的特征。分析Gnutella网络的网络弹性，包括对于随机攻击的容错性和对于选择性攻击的抗攻击性，并与ER模型和EBA模型进行了对比。Gnutella网络对于随机攻击具有很好的容错性，但是对于选择性攻击却显得脆弱。最后对网络弹性进行了理论分析，给出了网络在出现最大集团临界点之前的平均集团大小的公式解。 关键词：对等网络；无标度；网络弹性；脆弱性 中图分类号：TP393.02文献标识码：A 文章编号：1001-9081(2007)04-0784-04 0 引言 在过去的40多年里，科学家习惯于将所有复杂网络看作是随机网络。随机网络中绝大部分节点的连结数目会大致相同。1998年开展的一个描绘互联网的项目却揭示了令人惊诧的事实：基本上，互联网是由少数高连结性的页面串联起来的，80%以上页面的连结数不到4个，而只占节点总数不到万分之一的极少数节点，例如门户网Yahoo和搜索引擎Google等类似网站，却高达上百万乃至几十亿个链接。研究者把包含这种重要集散节点的网络称为无标度网络[1]。
具有集散节点和集群结构的无标度网络，对意外故障具有极强的承受能力，但面对蓄意的攻击和破坏却不堪一击[2]。在随机网络中，如果大部分节点发生瘫痪，将不可避免地导致网络的分裂。无标度网络的模拟结果则展现了全然不同的情况，随意选择高达80%的节点使之失效，剩余的网络还可能组成一个完整的集群并保持任意两点间的连接，但是只要5%―10%的集散节点同时失效，就可导致互联网溃散成孤立无援的小群路由器。 许多复杂网络系统显示出惊人的容错特性，例如复杂通信网络也常常显示出很强的健壮性，一些关键单元的局部失效很少会导致全局信息传送的损失。但并不是所有的网络都具有这样的容错特性，只有那些异构连接的网络，即无标度网络才有这种特性，这样的网络包括WWW、因特网、社会网络等。虽然无标度网络具有很强的容错性，但是对于那些有意攻击，无标度网络却非常脆弱。容错性和抗攻击性是通信网络的基本属性，可以用这两种属性来概括网络弹性。 对等网络技术和复杂网络理论的进展促使对现有对等 网络的拓扑结构进行深入分析。对网络弹性的认识可以使从网络拓扑的角度了解网络的脆弱点，以及如何设计有效的策略保护、减小攻击带来的危害。本文研究Gnutella网络的网络弹性，并与ER模型和EBA模型进行了比较，对比不同类 型的复杂网络在攻击中的网络弹性。当网络受到攻击达到某
▲判断：组织在管理中起着关键枢纽作用。（V） ▲判断：管理本质就是为了有效地实现管理目标的活动。（V） ▲环境：组织生存的土壤，它既为组织活动提供条件，同时也必须对组织的活动起制约作用。▲判断：任何组织的经营过程，实际上是不断在其内部环境、外部环境与经营目标三者之间寻求动态平衡的过程。（V） ▲判断：SWOT分析是最常用的内外部环境综合分析技术。（V） ▲判断：不是古埃及颁布的）古巴比伦：《汉穆拉比大法典》 ▲古罗马：建立了层次分明的中央集权帝国，实行一种把集权和分权相结合的连续授权制度。（判断V） ▲到中世纪，西方管理实践和管理思想都有了很大的发展。（判断：V） 道家：（老子） ▲道家管理思想的核心：无为而治（判断V） ▲商家的经营思想在我国管理思想史上占有重要位置。（判断V） ▲欧洲伟大社会学家韦伯的管理学理论，为管理学提供了一个科学的理论框架。（判断题）▲人际关系理论的诞生是从著名的霍桑试验开始的。（判断V） ▲判断题影响生产力最重要的因素是工作中发展的人际关系，而不是待遇和工作环境 ▲工人是社会人，而不是经济人。（判断题） ▲新型的领导能力在于提高工作的满足感。（判断题） ▲判断：决策是管理的核心，管理功能实质上是决策方案实施过程的体现。（V） ▲判断：决策就是从两个备选方案中选择一个方案的过程。（V） ▲判断：专家调查法是最科学的预测方法。（X） 管理者是在管理中指挥和领导他人活动的人们，他们构成了管理活动的主体。（判断）
判断：管理是人类社会协作劳动和共同生活的产物（V） 政府的管理具有典型的合法性和强制性（判断） 判断：“带着有色眼镜看人”是种应当克服的决策定型效应。（V） 判断决策是计划的前提，计划是决策的逻辑延续（V） 作为管理的载体和基本途径，组织对于管理具有基础性和工具性的意义（判断） 判断：非正式组织也有明确的组织结构类型。（X） 判断：任何组织在发展过程中，都会出现失误。（V） 社会生产在不断发展，但现代组织的规模和内部结构日趋简单。（X） 由于组织环境的不确定性，组织必须通过控制来及时了解情况，调整计划，修正目标。（V）判断：建立专门履行控制职能的机构是控制的功能。（X，应该是功能的基本条件） 判断：一种新制度是否优越的衡量标准是企业的效益是否提高X）。坚持收益大于成本的原则。 判断：只有市场创新，才能对消费者各类需求给予更大程度的满足X） 管理创新是一种手段、方式，而不是最终目的（判断）； 1、组织是以特定的结构形式存在和活动的。V 2、组织设计不仅是组织结构的设计和优化，更重要的是组织规范的制度建议。V （1）组织结构的刚性将减少内部组织成本（判断）。 （2）组织结构的刚性将减少委托代理成本（判断）。 （3）组织结构的刚性将增加外部交易成本（判断）。 单项选择题 2）内部环境：组织性质和人员状况等。（单选）
目录 1 软件介绍...................................................... １ 2 软件运行环境 ................................................. １ 3 软件安装步骤 ................................................. １ 4 软件卸载步骤 ................................................. ４ 5 软件使用...................................................... ４5.1、创建数据库.............................................................................................................................. ４ 5.2、创建数据数据表................................................................................................................... ６ 5.3、历史数据读取 ........................................................................................................................ ７ 5.4、查看历史数据、通道信息.............................................................................................. ８ 5.5、打印数据、曲线或图片输出 .................................................................................... １３ 5.6、数据实时采集 .................................................................................................................... １５ 6 软件使用中可能出现的问题与解决方法.................. １８6.1、不出现对话框 .................................................................................................................... １８ 6.2、数据库不能建立............................................................................................................... １８ 6.3、U盘不能数据转存........................................................................................................... １８ 6.4、U盘上没有文件 ................................................................................................................ １８ 6.5、U盘数据不能导入计算机；...................................................................................... １８
《法律基础知识与公安业务知识》模拟试卷一 一、单项选择题（共45题，每题1分） 1.下列说法正确的是( ) A. 世界上最早出现的宪法是美国的宪法B．世界上第一部成文宪法是英国的宪法C．欧洲大陆第一部成文宪法是法国宪法D．我国正在实施的宪法已经过5次修正 2.我国刑法关于溯及力问题采取的原则是（）。 A．从新原则B．从旧原则 C．从新兼从轻原则D．从旧兼从轻原则 3. 在侦查期间，发现犯罪嫌疑人另有重要罪行的，对其侦查羁押期限应（）。 A.经上一级人民检察院批准延长一个月 B.经省级人民检察院批准延长两个月 C.自发现之日起重新计算侦查羁押期限 D.由最高人民检察院报请全国人大常委会批准延期审理 4. 根据刑事诉讼法的规定，危害公共安全的案件由( )立案侦查。A.公安机关 B.国家安全机关 C.人民检察院 D.人民法院 5. 司法机关接到报案、举报、控告后，对不属于自己管辖的案件，应当（）。 A.告之举报人向有管辖的权单位举报B.决定不予受理 C.先接受再按管辖分工移送主管机关处理 D.先立案后再移送有管辖权的单位处理 6.行政处罚的功能在于（）。
A．预防B．惩罚与教育C．杜绝D．警示 7. 限制人身自由的行政处罚权只能由（）行使。 Ａ.人民法院Ｂ.公安机关 Ｃ.国务院Ｄ.全国人大及其常委会 8. 在某地方人民政府制定的行政规章中明确规定，将对某一违法行为的罚款权“委托”给某一组织。根据这一规章，行使行政处罚权的组织（）A．应以自己的名义实施行政处罚B．不能以自己的名义实施行政处罚C．可以自由地行使罚款权D．对自己的行为后果独立地承担法律责任 9. 李某自1997年4月起开始非法聚众赌博，至次年1月停止。1998年8月公安机关根据举报发现了李某的违法行为。下列哪一种说法是正确的？（）A．对李某违法行为不进行追究B．公安机关应对李某予以处罚C．李某系主动停止违法行为，可以从轻处罚D．若李某配合查处违法行为，应当减轻处罚 10. 某企业对县国税局作出的行政处罚决定不服，可以向（）申请复议。 A.该国税局的上一级国税局B.县人民政府 C.该国税局的上一级国税局和县人民政府D．该国税局的上一级国税局或县人民政府 11.1998年甲违反海关法的规定，走私香烟入境，尚未达到犯罪标准。2001年，甲走私的违法行为被举报。对甲的行为，应如何处罚？（）A．由公安机关处罚，予以罚款
投标文件管理软件（V2.0.0.3） 用 户 使 用 手 册 深圳市斯维尓科技有限公司 二〇一三年三月五日
目录 1引言 (3) 2 程序运行环境 (4) 3 程序安装 (4) 4 软件启动 (9) 5软件整体说明 (12) 6 软件操作说明 (15) 6.1导入查看招标文件 (15) 6.2新建投标文件 (16) 6.3投标文件的管理功能 (23) 6.4校对工程量清单 (29) 6.5转换投标文件 (30) 6.6 电子签章 (32) 6.7生成投标文件 (34) 6.8查看数字签名信息 (41) 7 程序卸载 (42)
1引言 编写本手册的主要目的是为东莞市建设工程交易中心电子评标系统的投标文件管理软件的使用提供帮助。 投标文件管理软件主要提供给投标单位使用。投标单位通过投标文件管理软件将工程招标文件的一些主要内容导出，根据招标要求制作投标文件；加入已经制作好的工程投标文件所包含的所有文档（包括：技术标文件、工程量清单、工程图纸以及其它文件等），并进行管理，对文件包进行CA数字签名以防篡改，并生成压缩加密的电子投标文件包的功能。 投标文件管理软件的使用总体流程如下图所示：
2 程序运行环境 ?硬件环境：CPU: P4 2GHZ 内存2G,硬盘80GB ?软件环境：Windows 2000/XP/Windows Server 2003 ?软件支持：OFFICE2007+PDF转换插件/OFFICE2010 ?网络环境：带宽10/100Mbps 3 程序安装 东莞市建设工程交易中心网站(https://www.doczj.com/doc/6317439722.html,/)上下载最新安装包，点击安装程序，安装程序引导用户进行系统安装，主要有以下步骤： 一、启动安装程序，进入安装系统欢迎界面。如下图：