What is an indirect relationship? Definition and examples
a relationship which plays such a prominent role in the methods and theories of the natural sciences. Buehler, in his linguistic theory, expanded the triadic system into a tetradic one speaker and listener, the direct as well as the indirect relation is reversed. Evolution is not senseless as the scientific theories suggest. To many philosophers, our science is intended to represent reality. . accounts which view scientific representation as at least a triadic relationship insofar . of a two-particle Newtonian model with an inverse square central force” (, 80). The relationship between two variables which move in opposite directions; when one of the variables increases the other variable decreases. In a business setting this can also refer to a relationship caused by changes in interfacing functional groups. However, changes made by group.
Similarly, a scientist could not claim that there is similarity between the color of a mathematical model and the color of a species of bacteria since a mathematical model does not have any color. Notice that it is insufficient to merely specify the respects in which a model is similar since similarity can come in degrees. Of course, there is a whole spectrum of degrees of similarity on which any particular similarity can fall.
Here, the relevant respects are the position and velocity of the earth and moon. These respects and degrees thus give us an account of how we should think of the similarity between the model and the target system. Giere uses similarity to describe the relationship between models and the real-world systems they represent, and sometimes between different models one model may be a generalization of another, and so on.
Theories themselves are constituted by a set of these models as well as some hypotheses that link the models to the real world which define the respect and degree of the similarity between the models and their targets. More recently, Michael Weisberg has argued for a similarity account of representation. In brief, his view argues that two sets of things be distinguished in both source and target: In distinguishing these sets, an equation can be written in which the common attributes and mechanisms can be thought of as the intersection of the attributes of the model and of the target system, and the intersection of the mechanisms of the model and target system.
The dissimilarities can also be identified in a similar fashion. He adds some terms to these sets which are weighting terms and functions. These allow the users to indicate which similarities are more important than others. Rewriting the equation as a ratio between similarities and dissimilarities will result in a method by which we can make comparative judgments about different models.
In this way, we will be able to say, for example, that one model is more or less similar than another.
What is indirect relationship? definition and meaning - posavski-obzor.info
Critiques of Substantive Accounts While similarity and isomorphism continue to have some support in the contemporary literature especially in modified versions; see below, section 3cthe versions described above have faced serious criticisms. Many models in science do not accurately reflect the world, and, in fact, the model is often viewed as particularly useful because of not in spite of the misrepresentations.
Nancy Cartwright has famously argued for a fictional account of modelling and made this case for the laws of physics.
Others have shown that similar things are true in other scientific domains Weisberg a. When the theories are intentionally inaccurate, there will be difficulty in explaining the way in which these theories are representational as scientists and philosophers often take them to bewith reference to isomorphism or similarity. It must be the case that it is not necessary for representation, given that scientists often take certain theories to be representative of their real-world targets even though there is no isomorphic relationship between the theory and the target system.
The same is true of similarity. Using his example, suppose that there is an artist painting an ocean view, using some blue and green paints.
This painting has all sorts of similarities to the ocean view she is representing, one of which is that both the painting and the ocean are on the same relative side of the moon, are both in her line of vision at time t, share certain colors, and so forth.
But which ones are relevant to its being representative and which are more contingent is up to the discretion of the agent who takes it to be representative of the ocean view in certain respects as Giere argued. But if this is the case, then it turns out that A represents B if and only if A and B are similar in those respects in which A represents B.
This ultimately leaves representation unexplained. Supposing we can give some account of salience or attention or some other socially-based response to this first problem which seems possiblewe are left with the problem that plenty of salient similarities are non-representational. The bull, crying mother, eye, knife, and so forth, are all similar to certain real-world objects. But the painting is not a representation of these other things.
It is representing some of the horrible atrocities of Franco. Consider the first, similarity. Take any given manufactured item, for example, an Acer C Chromebook, a computer which is similar to many other computers hundreds of thousands. Notice that the fact of its similarity is insufficient to make it represent any of the other computers. In fact, it seems as though there are hypotheses which define the relevant respects and degrees of similarity between the computers: All the same, even with these hypotheses which give respects and degrees, we would not want to say that any given computer represents the others.
The non-sufficiency problem holds for isomorphism as well. Suppose someone were to write down some equation which had various constants and variables, and expressed certain relationships that held between the parts of the equation. Suppose now that, against all odds, this equation turns out to be isomorphic to some real-world system, say, that it describes the relationship between rising water temperatures and the reproduction rate of some species of fish which is native to mountain streams in the Colorado Rockies.
To many, it appears to be counterintuitive to think that representations could happen accidentally. However, if isomorphism is sufficient for representation, then we would have to admit that the randomly composed equation does represent this fish species, even if no one ever uses or even recognizes the isomorphic relationship. There are other arguments against these views in general, an important one being that they lack the right logical properties. Representation is non-symmetric, so when some A represents B, it does not follow that B represents A.
A does not represent itself. Since isomorphism is reflexive, transitive, and symmetric, and similarity is reflexive and symmetric, they do not have the properties required to account for representation.
Representation, Scientific | Internet Encyclopedia of Philosophy
There are replies to these arguments on behalf of the substantive views. First, there is a general question about whether or not we are justified in making inferences from representation in art to representation in science. But, it should not be taken as given that what holds in art must translate to science. In fact, in many cases, the practices in art seem to be quite different from the practices in science.
That is to say, while similarity or partial isomorphism might not be the whole story, they are at least part of the story. Replies have been made to the other arguments as well. Adam Toon discusses some of the ways in which supporters of a similarity account of representation might respond to criticisms. Bartels defends the homomorphism account against these criticisms. Deflationary and Pragmatic Accounts If, as these scholars have argued, these substantive views will not work to explain scientific representation, what will?
Deflationary accounts are typically marked by a couple of features. First, a deflationary account will deny that there are any necessary and sufficient conditions of scientific representation, or if there are, they will lack any explanatory value with regard to the nature of scientific representation. Second, these accounts will typically view representation as a relationship which is deeply tied to scientific practice. Already we can see that these views will be quite different from the substantive views.
Each of these views was substantive in the sense that they gave necessary and sufficient conditions for representation. There was also a distinct way in which these views were detached from scientific practice, since whether something was representational had little to do with whether or not it was accepted by scientists as representational and more to do with the features of the source and target. In each case, it was a relationship that was entirely accounted for by features of the theory or model and the target system.
As Knuuttila describes it, these were all dyadic two-place accounts insofar as the relationship held between only two things. The deflationary accounts take a markedly different direction by moving to at least a triadic three-place account of representation. In some cases, the views that have developed have followed the general lead of many deflationary views in giving a central role to the work of an agent in representation.
These views do not qualify as deflationary, given that they still give necessary and sufficient conditions of representation. Given the importance of the role of agents and aims, we might call these views pragmatic. Although pragmatic and deflationary views are importantly distinct in their aims, they share many common threads and in many cases, the views could be reinterpreted as deflationary or pragmatic with little effort.
As such, they will be grouped together in this section. The DDI Account consists of three parts: Denotation is the way in which a model or theory can reference, symbolize, or otherwise act as a stand-in for the target system. In each case, the model denotes something else; it stands in for some particular concrete object, some type of theoretical object, or some type of dynamical system.
We might think this relationship sufficient for representation, since the fact that scientists treat certain objects or parts of models as being stand-ins or symbols for some target system seems to answer the question of the relationship between a model and the world. Hughes, though, thinks that in order to understand scientific representation, we need to examine how it is actually used in scientific practice.
This requires additional steps of analysis. The nature of this salience is such that it allows users to draw certain types of conclusions and make certain predictions, both novel and not. This is demonstration in the sense that the models are the vehicles through which or in which these insights can be drawn or demonstrated, physically, geometrically, mathematically, and so forth This requires that they be workable or used in certain ways. The final part of the DDI Account is interpretation.
It is insufficient that the models demonstrate some particular insight. The insight must be interpreted in terms of the target system. That is to say, scientists can use the models as vehicles of the demonstration, but in doing so, part of the representational process as defended in the DDI Account is that scientists interpret the demonstrated insights or results not as features of the model, but rather as features which apply to the target system or at least, the way scientists are thinking of the target system.
In summary, with denotation, we are moving in thought from some target system to a model. We take a model or its parts to stand in or symbolize some target system or object. In demonstration, we use the model as a vehicle to come to certain insights, predictions, or results with regard to the relationship that holds internal to the model.
It is in interpretation that we move from the model back to the world, taking the results or insights gained through use of the model to be about the target system or object in the world. As he describes it there, this account involves two parts. The first part is what he calls representational force.
Representational force can exist for a number of reasons. One way to get representational force is to repeatedly use the source as a representation of the target. Another way is in virtue of intended representational uses, that is, in virtue of the intention of the creator or author of some source viewed within the context of a broader scientific community.
Oftentimes, the representational force will occur as a combination of the two. So, for example, in the upper left-hand corner of my word processor is a little blueish square with a smaller white square and a small dark circle inside of it it is supposed to be an image of a floppy disk. This has representational force insofar as it allows me to go from the source the image of the floppy disk to the target a means of saving the document which I am currently writing. In this case, the representational force exists in virtue of both the intended representational uses the creators of this word processor surely intend this symbol to stand in for this activity as well as repeated uses I am part of a society which has, in the past, repeatedly used an image of a floppy disk to get to this target, not only in this program but in many others as well.
It is also contextual: On his view, scientific representations are subject to a sort of objectivity which does not necessarily exist for other representations, for example, the example above of the save icon. The objectivity is not meant to indicate that there is somehow an independent representational relationship that exists in the world when scientists are engaged in scientific representation.
Instead, the objectivity is present insofar as representations are constrained in various ways by the relevant features of the targets system which is being represented. That is, because there is some real feature which scientists are intentionally trying to represent in their scientific models and theories, the representation cannot be arbitrary but must respond to these relevant features.
This second feature requires that informed and competent agents be led to draw specific inferences regarding the target. First, the source must have internal structure such that certain relations between parts can be identified and examined.
It is in virtue of these norms of the practice that an agent will be able to draw the relevant and intended inferences, making the representation a part of that particular scientific practice. Of course, he takes his view to be deflationary, so these are not to be understood as necessary and sufficient conditions of the capacity for surrogate reasoning, but rather features which are frequently in place.
Consider an example of a mathematical model, for example the Lotka-Volterra equation. The model is supposed to be representational of predator-prey relationships. To account for this, he argues that there is another feature of the model, which is the capacity to allow for surrogate reasoning. In this case, that means that individuals who examine or manipulate the model in terms of its parts the multiple variables will be able to draw certain inferences about the nature of real-world interactions between predators and prey the parts of the target system.
These insights will occur in part due to the nature of the model as well as the norms of scientific practice, which means that the inferences will be non-arbitrarily related to the real-world phenomena and will afford us to recognize certain specified inferences of scientific interest. He is explicit in his claim that the interpretational view he is defending is not a deflationary account, but is rather a substantive version of the inferential account insofar as he takes the account to give necessary and sufficient conditions of representation.
All the same, the account he defends is clearly pragmatic in nature. The first is mere denotation, in which some arbitrarily chosen sign is taken to stand for some object. He gives the example of the logo of the London Underground denoting the actual system of trains and tracks. The London Underground logo does not have this feature since no one would be able to use it to figure out how to navigate. A map of the London Underground, on the other hand, would have this feature insofar as it could be used by an agent to draw these sorts of inferences.
Whether or not a representation is faithful is a matter of degree, so something will be a completely faithful epistemic representation provided all of the valid inferences which can be drawn about the target using the source as a vehicle will also be sound. Notice this does not require that a model user be able to draw every possible inference about the target, but rather that the inferences licensed by the map that are drawn will be sound inferences both following from the source and true of the target.
In this sense, a map of the London Underground produced yesterday will be more faithful than one produced in the s. Using this framework, Contessa goes on to describe a scientific model as an epistemic representation of features of particular target systems The scientific model will be representational for a user when she interprets the source in terms of the target. Here is where Contessa draws on the distinction of faithfulness. Since models are often misrepresentations and idealizations as has been discussed above, they need not be completely faithful in order to be useful.
This is not the end of the story, though, because the circumstances also play an important role in understanding whether or not something is a scientific representation. Of most importance, given their role in the substantive views as described above are recent advances made by van Fraassen and Giere.
Agent-Based Isomorphism The view of isomorphism commonly attributed to van Fraassen, which was described above, was the one drawn from his book, The Scientific Image. More recently, van Fraassen has presented an altered account of representation, which places much more emphasis on the role of an agent in his Though he does not take himself to be offering any substantive theory of representation, he does call this the Hauptsatz or primary claim of his account of representation: Van Fraassen notices that this places some restrictions on what can possibly be representational.
Mental images are limited, because they are not made or used in some way. That is to say, we do not give our mental states representational roles. Similarly, there is no such thing as a representation produced naturally. What it is to be a representation is to be taken or used as a representation, and this is not something that happens spontaneously without the influence of an agent.
Van Fraassen also notices an important distinction in two ways of representing: When scientists take or use some source to be representational, they take it to be a representation of some target. This target can change based on context, and sometimes scientists might not even use the source to be a representation at all. But we could also use that graph to represent other phenomena, perhaps the acceleration of an object as it is dropped from some height.
Part of what this captures is the way in which our perspectives can change the way in which we are representing a particular appearance. Thus, by using a source in some distinct way, we can represent some particular appearance of some particular phenomena. In intentionally using a source as a representation, scientists do not only make it a representation of something, but they also represent it in a certain light, making certain features salient.
This is what van Fraassen calls representation as. Two representations can be of the same target, but might represent that target as something different.
Van Fraassen offers an example: Similarly, we might represent the growth of bacteria mentioned above as an example of a certain sort of growth model or as the worsening of some infection as it is seen as part of a disease process. Of course, all of this is very general, which van Fraassen acknowledges. Nonetheless, he still maintains that the link between a good or useful representation and phenomena requires a similarity in structure.
As it stands, then, there is still an appeal to isomorphism present in his account: Just as before, we have an account of representation which relies on isomorphism between the structure of the theoretical models and the structure of the phenomena. All the same, this is still a markedly different view from his earlier view described above. No longer is it the isomorphism or structural relationship alone which is representational. Agent-Based Similarity Ian Hacking has famously argued that, in philosophical discussions of the role and activity of science, too much emphasis is put on representation.
Instead, he suggests that much of what is done in science is intervening, and this concept of intervention is key to understanding the reality with which science is engaged. All the same, he still thinks that science can and does represent. Representation, on his account, is a human activity which exhibits itself in a number of different styles.
It is people who make representations, and typically, they do so occurs in terms of a likeness, which he takes to be a basic concept. Representation in terms of likeness, he thinks, is essential to being human, and he even speculates that it may have played a role in development like many think language did.
In creating a likeness, though, he argues that there is no analyzable relation being made. Representation on his view is not interested in being true or false, since the representation precedes the real. Gierehas also made pragmatics more central and explicit to his account of scientific representation. He claims that in attempting to understand representation in science we should not begin with some independent two-place relationship, which substantially exists in the world.
Instead, we should begin with the activity of representing. If we are going to view this activity as a relationship, it will have more than two places. He proposes a four-place relation: Here, S will be some agent broadly construed, such that it could be some individual scientist, or less specifically some group of scientists.
X is any representational object, including models, graphs, words, photographs, computational models, and theories. W is some aspect or feature of the world and P are the aims and goals of the representational activity; that is, the reasons why the scientist is using the source to represent the target.
Giere identifies a number of different potential purposes of representation. A relationship is established in graphs between or among variables. Variables are quantities of data that change, and from which we want to establish trends. Variables are either independent or dependent.
Variables and relationships in economics include the price of a good or service in relation to quantity demanded or supplied of a good or service, annual consumption expenditure in relation to annual real GDP, the Canadian interest rate in relation to annual planned expenditures of consumers, business, and government. We need to establish the differentiation between independent and dependent variables.
For example, in social research, you may want to establish a relationship between height and weight. You could show that the weight of an individual is the dependent variable, dependent on the height of an individual, and height is an independent variable.
More height is going to mean, ceteris paribus other things being equalgreater weight. Less height is likely going to mean a lower weight. A dependent variable changes in relation to an independent variable, while an independent variable changes, for purposes of analysis, freely in value.
A relationship could be thought of as a connection; you connect two variables to establish an association. There are two relationships you need to know about in economics. A positive or direct relationship is one in which the two variables we will generally call them x and y move together, that is, they either increase or decrease together. An excellent example is the price of steel, and the response of steel suppliers to bring steel to the market; as the price increases, so does the willingness of producers to bring more of the good to the market.
The example we gave of the relationship between height and weight is a direct or positive relationship. In a negative or indirect relationship, the two variables move in opposite directions, that is, as one increases, the other decreases.
Consider the price of coffee and the demand for the good. As the price of coffee, for example, goes to higher and higher levels, we can predict that people will substitute tea or hot chocolate for it, and buy less. As the price of coffee declines, people will buy more and more of it, and quite possibly buy more than they would regularly buy, and store or accumulate it for future consumption, or to sell it to others.
This relationship is negative or indirect, that is, as the price variable typically, in economics, the y variable increases, the quantity variable typically, the x variable decreases; and, as the price variable decreases, the quantity demanded increases. These relationships between positivly- and negatively-related variables are demonstrated in the graphs Figure 1 which follow, positive first and negative second: What is the value of graphs in the study of economics?
Graphs are a very powerful visual representation of the relationship between or among variables. They assist learners in grasping fairly quickly key economic relationships. Years of statistical analysis have gone into the small graph you can examine to learn about key forces and trends in the economy.
Further, they help your instructor to present data in a way which is small-scale or economical, and establish a relationship, frequently historical, between variables in a certain kind of relationship. They permit learners and instructors to establish quickly the peaks and valleys in data, to establish a trend line, and to discuss the impact of historical events such as policies on the data that we wish to analyze.
Types of Graphs in Economics There are various kinds of graphs used in business and economics that illustrate data. These include pie charts segments are displayed as portions, usually percentages, of a circlescatter diagrams points are connected to establish a trendbar graphs results for each year can be displayed as an upward or downward barand cross section graphs segments of data can be displayed horizontally.
You will deal with some of these in economics, but you will be dealing principally with graphs of the following variety.
Certain graphs display data on one variable over a certain period of time. For example, we may want to know how the inflation rate has varied in the Canadian economy from We would choose an appropriate scale for the rate of inflation on the y vertical axis; and on the x horizontal axis show the ten years from to with on the left, and on the right. We would notice right away a trend. The trend in the inflation rate data is a decline, actually from a high of 5.
We would see that there has been some increase in the inflation rate since its absolute low inbut not anything like the high. And, if we did such graphs for each of the decades in Canada sincewe would see that the s were a unique decade in terms of inflation. No decade, except the s, shows any resemblance to the s. We can then discuss the trends meaningfully, since we have ideas about the data over a major period of time.
We can link the data with historical events such as government anti-inflation policies, and try to establish some connections. Other graphs are used to present a relationship between two variables, or in some instances, among more than two variables. Formation of New Ties The basic problem of tie formation prediction revolves around the idea of predicting when a tie will form between two non-connected individuals.
Intuitively, the further they are in the network, and the less information we have about their interactions makes this prediction task more difficult. To this end, we studied indirect ties between individuals and how they can improve the performance of this prediction task.
We found that indirect ties carry a significant power in predicting when new ties will form between two individuals, especially when we utilize information such as the number of indirect ties connecting the two individuals and also the strength of the relationship between each of them and their common friend s. Moreover, we found that the implicit strength of an indirect tie positively correlates with the speed with which a new tie is formed, and further, that this newly connected pair of individuals will have more interactions when this implicit strength is higher.
Also important is that this prediction power remains fairly high even when we consider 3-hop indirect ties: Diffusion of New Information Diffusion of new information is a fundamental process in social networks and has been extensively studied in the past.
In fact, some studies have shown that the evolution of a network is affected by the diffusion of information in the network and vice versa. However, diffusion of information has been typically studied in a step-wise fashion, that is, how new information will propagate in the network step-by-step, or from individual to individual.
Indeed, it would be ideal to predict how new information will diffuse in the network much earlier in advance. To this end, we studied how indirect ties can be used in this diffusion prediction task.