Ch 3 The Mechanics of Change Essay Example

Ch 3: The Mechanics of Change Paper 3.8 The mechanics of change So far in this review, change has been discussed without explicitly providing a definition or nomenclature for what exactly is meant by the term. For example, within the neoclassical theory of production and consumption, economic change is something that is mostly thought of happening in a marginal fashion ‘little-bit by little-bit’ an idea reinforced at a more formalised level by the common analytical use of differential calculus. In this type of analysis, change occurs up and down demand and supply curves, expanding and contract output, adjusting to more efficient technologies and processes, with prices going up and down, as the system self-adjusts towards equilibrium. The main source where flexibility is limited is the case of fixed costs and plant size when moving between the short and long-run. At the macroeconomic level, change can be seen as movements along, or shifts in the production function, and expressed through aggregate measures such as the level and grow th rate of GDP, giving rise to notions such as the ‘steady-state’ growth rate with its accompanying policy recommendations. An alternative view of change is presented by the theory of path dependency, where a decision made today may preclude, or make it difficult, certain decisions tomorrow once a specific technological path has been established. For example witness the difficulty some countries, such as Germany, in moving away from nuclear power despite strong policy statements to do so. In this case change often takes on a qualitative dimension and is conceptualised as involving very non-marginal shifts in economic resources. As we move towards evolutionary theory, the definitions around change and its components become more refined. For example, while essentially meaning â€Å"change† McKelvey and Holmà ©n (2006:2-3) use the term economic transformation to mean: a non-reversible process, encompassing quantitative and qualitative changes in components and connections, driven by opportunities and innovations. Such economic transformation may well be driven by processes of complexity and self-organisation as well as processes of actors acting, adapting to contexts. Moreover, the concept of transformation, as used here, may result from very different processes, including ones driven by very large and discontinuous changes as well as ones driven by very small changes, which follow upon an existing trajectory. We will write a custom essay sample on Ch 3: The Mechanics of Change specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Ch 3: The Mechanics of Change specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Ch 3: The Mechanics of Change specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Less all-encompassing, Geels and Kemp (2006) in the same volume provide greater clarity still by distinguishing three different types of change according to its scope and underlying mechanisms: Reproduction is seen to be a type of incremental change occurring along existing trajectories. Reproduction only involves change at the regime level (Figure 3.7), not at the landscape or niche levels. The existing sociotechical system forms a stable context for the interaction of social groups. Existing rules are reproduced by incumbent actors and elements of the system are refined. The orientation of dominant actors, key technology and knowledge base do not change fundamentally. This situation at the regime level is stabilised by the high sunk cost of existing investments, role expectations in networks, regulations and standards, contracts and cognitive routines (rules of thumb). Despite changes being small, incremental innovations do occur and can result in major productivity improvements over time. Rosenberg (1982:62) describes a similar process: A large portion of the total growth in productivity takes the form of a slow and often invisible accretion of invisible small improvements in innovations () such modifications are achieved by unspectacular designs and engineering activities, but they constitute the substance of much productivity improvement and increased consumer wellbeing in industrial economies. The second category of change is transformation. This is change in the direction of trajectories involving action at the regime and landscape level, but with little influence from niches. Change in this area can be driven by exogenous landscape 164 changes which create pressure on the existing regime leading to a reorientation of innovative activities; or by endogenous changes in regime rules. For example, shifts may occur in technical problem agendas and visions underpinning goals and guiding principles of engineers and scientists; relative costs and incentive structures may be altered through regulations and policies, shifting the perceptions of where opportunities lie. This reorientation is not likely to occur in a mechanical way but be subject to negotiations, power struggles, and shifting coalitions of actors. In the face of growing pressure for change, incumbent actors are likely to downplay the need for transformation, which changes in social networks often vital to start the transformation process. New actors may help particularly to challenge existing assumptions and place issues on the problem agenda (Van de Peol, 2000) and by expressing concerns over negative externalities of the existing system, precipitate a response from within the regime (Van de Poel, 2003). However, these new players (or outsiders) do not develop competing technologies to replace the existing system, so the survival of the incumbent regime is not threatened, and it is they who enact the change in trajectory. Over time, a new system may grow out of the old one through cumulative adjustments. The third type of change is termed transition, and is a change describing the shift from one socio-technical system to another. This is seen to be a discontinuous shift to a new technological trajectory and encompasses interactions between niches, the regime and the landscape, to which incumbents are unable to adjust. For example, a change in the economic landscape may create major problems for incumbents in the existing regime. Unable to adapt through transformation, a window of opportunity opens for new innovations, which have developed in market niches to be carried forward by new social groups. The transition invokes shifts in the knowledge base, technologies, infrastructure, regulatory framework, consumer behaviour and social groups. If the new innovation breaks though and replaces the existing system, this will be accompanied by creative destruction and the downfall of some incumbent actors. Once the transition has taken place, a new period of dynamic stability and reproduction sets in. While it would be wrong to suggest that these meanings are universally attached to these words in the evolutionary literature (for example, â€Å"transition†, has been used to describe movement between two states of the same system); for the purposes of clarity, this review will follow the definitions set out by Geels and Kemp (2006). 3.9 The contribution of network or systems theory â€Å"System† is another word we have assigned implicit meanings to throughout this review without formally providing a definition. For example, we have used the neoclassical micro-economic utility and profit maximising market system of the individual or firm bounded by price and quantity; there is the macro-economic system inherent in the models of growth theory; and there are the sociotechnological systems conceptualised in the evolutionary, institutional approach of path dependency theory. While the â€Å"market-system† is often the core conceptual construct in which the action of economic life tends to take place, it is very often assumed to be simply â€Å"out there† and is very loosely defined, if at all in standard analysis. In the neoclassical model, markets are usually portrayed as the context within which the acts of buying and selling take place. In this market consumers and producers ‘optimise’ to make decisions taking as cues cost and price information which drives the economy to a unique equilibrium (Figure 2.1). Alternatively, if we view the economy as an â€Å"evolutionary system† where firms compete in struggle for profits or survival, then we need to specify the processes for variation, selection and replication. As pointed out by Coyle (2006:188) the adoption of such an evolutionary system as an analytical construct for the economy raises a number of questions, not least: what are the sources of change? What are the criteria for a variation to succeed in a given environment – how is â€Å"fitness† specified and what are the mechanisms for selection? How are â€Å"successes† passed onto other business units? What â€Å"units† should we be analysing – the firm? the manager? the market penetration of a product? How does the economic environment which shapes and constrains these actions evolve and how do the units interact with one another? To begin to address these questions, we will turn now to network or systems theory which has recently become a focus for economic research in this area (e.g. Beinhocker, 2007). This builds on a long tradition in physics, computer science, biology and sociology, and is underpinned by the work of the Hungarian mathematicians Paul Erdà ¶s and Alfrà ©d Rà ©nyi in the 1950s and 60s (for a review see Watts, 1999). The basic framework can be set out as follows: Consider the following object: Figure 3.4 A node Nodes are fundamental units of graph theory, which, in the abstract, can be treated as featureless and indivisible objects. Alternatively, they can also be assigned qualitative structure depending on the application they are being used for. For the purposes of this example, consider the node above as representing either an individual, social group, or firm: an economic agent. Put two nodes together and we have a basic system. Now, depending on the nature of the relationship between these nodes we can describe either a hierarchical network, or an interconnected network (Beinhocker, 2007: 155). Figure 3.5 Nodal relationships In the hierarchical system, a command node issues instructions to a subordinate node. Information regarding the outcome of these instructions will flow back to the command node through the subordinate node, but the instructions flow only one way. In an interconnected system, directions and response information can flow A node Hierarchical system Interconnected system 168 back and forth from either node. These basic systems can be built into something more complex: Figure 3.6 Two basic systems: hierarchy versus interconnectivity Within the hierarchical system, layers of command form from the top down, nodes further down the hierarchy receive commands from above, and then send response information back up the hierarchy to await further instruction. An advantage of a hierarchical system is that the density of connections are limited, thereby increasing the predictability of decision-making and enabling system-wide directional changes to be made more easily by the highest level command node. Consider a simple example: let us imagine each node represents an individual in an organisation, which has recently decided to implement a new strategic decision, say to adopt energy efficiency measures. If we take the figure above with 40 nodes, this means that in the extreme case of no hierarchy and a completely interconnected network, 1600 meetings would have to take place and everyone agree to the course of action with everyone else. Alternatively, in a hierarchical structure, a command is issued from the CEO which gets passed down through each layer of management which meets with their subordinates, agrees, then passes the information on to the Hierarchical system/network Densely interconnected system/network â€Å"edges† next layer and so on. In the case of the figure above we have one meeting between the CEO and his top lieutenants; then three meetings between these lieutenants and the lowest level of management who have nine meetings with their staff. All up 13 meetings are required to coordinate the entire system. On the other hand, the disadvantages of hierarchy are that information may degrade as it moves up and down the levels (think of a game of ‘Chinese whispers’), the highest command node may become out of touch with reality at the lowest levels, and a poor performer at the top can do a lot of damage to the system. Within a socio-economic context, examples of such structures might exist at the household level, in large corporations or government bureaucracies; or even extend to the entire economy, as in the case of the planned economy of the former Union of Soviet Socialist Republics. In an interconnected network, there are no control nodes, instead each node communicates to the other nodes it is connected to which may or may not respond depending on their own characteristics. Information is decentralised and dispersed around the system travelling between nodes that are linked. A common way to explore such systems is by assigning nodes a value of 0 or 1 (no or yes, for example) dependent on a set of rules. Such Boolean networks, have been a focus for research, particularly the work of Stuart Kauffman and the Santa Fe Institute (Kauffman, 1993). In the social sciences, such interconnected networks have been used as a metaphor for the market economy, where individuals trade with one another as rational utility or profit maximising agents. The nature of network interactions is voluntary and reciprocal, decentralised and complex, so that it is impossible for any one agent to understand it in its entirety. An important corollary of this type of system is the 170 principle of subsidiary: that decisions should be made by the node which is affected by the outcome of that decision. This idea has been used to support the notion of free markets as the antithesis to a hierarchical command and control system defined by rules from above. For example, Hayek (1974) argues that the market system coordinates: †¦a sum of facts which in their totality cannot be known to the scientific observer, or to any other single brain. It is indeed the source of the superiority of the market order, and the reason why, when it is not suppressed by the powers of government, it regularly displaces other types of order, that in the resulting allocation of resources more of the knowledge of particular facts will be utilized which exists only dispersed among uncounted persons, than any one person can possess. Another important corollary of this type of analysis is as a way of quantifying network effects. In the neoclassical analysis these were modelled as network externalities: when the increased use of a good, increases the value of further usage. Classic examples include, establishing a telephone or fax machine network, or the QWERTY keyboard. Here the benefit to each successive customer of choosing one technological variant over another is greater the larger the number of users of that technology. Stuart Kauffman (1993) has used this logic to derive a theory which seeks to explain tipping or turning points. He argues that a transition phase is initiated in networks when the ratio of the number of edges or connecting relationships with the number of nodes is equal to one. At this point a network goes from being sparsely connected to densely connected. This type of analysis can then be used to model economic change not as a smooth linear process, as in the neoclassical model (based on calculus), but as a non-linear process. It has been used to explain why, for example, social networks on the internet suddenly take off and others do not; why the stock market is so volatile; why fashions emerge; and why political movements can swing from obscurity to popularity in a short space of time (e.g. Farrell, 1998). A related advantage of this logic is that it can also help explain the observation of path dependency. As discussed above, new technologies, especially in energy markets, often require a vast amount of complementary infrastructure, products and other services in the supply chain. Thus decisions to support a particular technological pathway can induce similar supportive decisions in other related networks. For example, the shift from the internal combustion engine vehicle to electric vehicles may be particularly costly and difficult due to the threat this posses to the established related industries of manufacturers, mechanics, engineers and labour unions. Within the evolutionary economic model the â€Å"selection environment† is taken as the market within which economic agents operate (Metcalfe, 1998). Much more than the neoclassical place of exchange and interaction between consumers and producers, in the evolutionary model this also includes everything else which goes into the formation of market institutions: governments, regulators, lobbyists from rival firms, environmental pressure groups, universities, international trade agreements, law courts, corporate governance, federal-state relations and the impact of politics and events such as natural disasters and wars – most of which the neoclassical model leaves as exogenous despite their profound impact on economic life. Here there is an important point of differentiation between economic evolution and the notion of evolution as applied to biology. In biology evolution is perceived as being a blind process driven forward by random variety upon which the selection environment acts upon – a process of â€Å"design without the designer† an emergent, self organising order with no role for a central organising intelligence (such as a God). However, with respects to social systems Beinhocker (2006:249) points out: there is nothing fundamental in the nature of the evolutionary algorithm that says intentionality and rationality cannot play a role, nor does anything say that the process must be completely random. At its core, evolution is an iterative process of experimentation, selection, and then amplification of things that work. What Beinhocker argues is that in human society evolution is a goal-oriented purposive process where we consciously try to effect evolutionary outcomes: whether this is to gain market share, win an election – or at the biological level simply to reproduce and pass on our genes. In the economy, this means consumers, producers, technologies and the market institutions of their selection environment all are seen to co-evolve. This means that in addition to the principles of variation, selection and replication, we need to add interaction in a self-adaptive system. People, firms and other institutions become caught up in a self-conscious and mutually reinforcing system as they seek to mould their selection environment. These notions have given rise to the concept of the techno-economic paradigm (Freeman and Louca, 2001), and reviewed by Coyle (2006:195): The firms in an industry shape their own environment through trade associations, lobbying, standard setting, links with universities, law suits and so on. Political action and the legal framework are vital to the environment. The law of tort and the limited liability company played a vital role in nineteenth century industrialisation, for instance. Cars and airplanes needed governments to build roads and airports, to set the rules of the road and run air traffic control. The shaping of the competitive environment is also cumulative the invention of computers gave birth to computer science, and computer scientists develop the new innovations in the computer industry. However, what was once a source to promote system change, as new technoparadigms spread into the economy, can also become a source of stability as these new agents start to put more effort into tailoring their environment, such as lobbying for protection from overseas competition, or tax concessions, rather than investing in new research and development. The inherent difficulty of change – be it at the individual or firm level – is what is behind Schumpeter’s notion of creative destruction – that the inbuilt resistance to change builds up until firms are no longer to control the environment around them and the system suddenly reaches a turning or tipping point which brings in a new competing system of economic actors (see Gladwell, 2000). From this discussion on systems, we have begun to draw together several strains of research which seek to understand the mechanics of economic change. While it is clear that there is not, as yet, any universal synthesis for this body of work as there is in the neoclassical model presented earlier –with different authors emphasising different faces of the same evolutionary processes using a variety of terminology we will now move onto a body of literature that does offer a framework for synthesising many of these concepts: Strategic Niche Management and its related Multi-Level Perspective. 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Tips For Using An APA Format To Prepare For Your Exam

Tips For Using An APA Format To Prepare For Your ExamAre you one of those many students who need a good explanation of a particular subject that you are studying? If so, you should consider using an APA format to help you prepare for your future exam. This format can help you make your exam more interesting and productive. There are several advantages to using this format, and we will look at them here.An APA essay has no inherent rules, which makes it easy to write. The format gives you the freedom to use your own style as you wish. Writing from experience can be more effective than trying to make your words follow a formal grammar. There is no right or wrong way to do it, and it is up to you to decide how to present your ideas in a clear manner. It allows you to focus on your particular topic rather than trying to add too much knowledge in a matter of time.This is a popular format that many students use because it is easier than writing an essay about something that you have experi enced personally. This can also work to your advantage if you need to teach someone about a certain subject, since you can write a less formal and informal essay. When writing this format, there is a sense of humor that is not available in formal academic writing. Many people find it easy to enjoy the experience of learning. This can be a great way to retain the information you learn. Just think about what would happen if you did not write an essay about something you have actually experienced?You can also find some methods to make the content more interesting by making your sample expository essay into a book. You can do this by using the format in a variety of ways and adding pages and page numbers. You could also include a guide to the specific topics that you will be covering in your essay. This will help your reader remember the subject well. This is a wonderful format to use for a book or any other publication that will be published by you.You may also want to use the sample e xpository essay as part of a sample exam. This format can be useful if you are teaching a particular course. You can use the format to give a quick explanation of the content you will be discussing. The format is also great for teaching new students because it gives them a quick and easy way to understand the material they are learning. Most students will need to look up certain terms and make use of some of the terminology they learn.Some students find that when they take a quick look at the sample expository essay, they realize that they can remember a lot more about the topic they are studying than they originally thought. You might find that some of the concepts are even used in real life. The possibilities are endless. One great thing about using this format is that there is no limit on the number of topics you can write about. You can change the format to fit whatever you need to teach.For students looking for some tips on how to write an APA format sample essay, there are a f ew places to start. They include searching on the internet, and talking to a guidance counselor.