community. The innovations are usually concerned with the improvement of quality of life or the reform of organizational or social structures. Benefits versus costs Benefits of an innovation obviously are the positive consequences, while the costs are the negative. Costs may be monetary or nonmonetary, direct or indirect. Direct costs are usually related to financial uncertainty and the economic state of the actor. Indirect costs are more difficult to identify. An example would be the need to buy a new kind of pesticide to use innovative seeds. Indirect costs may also be social, such as social conflict caused by innovation. Marketers are particularly interested in the diffusion process as it determines the success or failure of a new product. It is quite important for a marketer to understand the diffusion process so as to ensure proper management of the spread of a new product or service. Intended versus Unintended The diffusion of innovations theory has been used to conduct research on the unintended consequences of new interventions in public health. In the book multiple examples of the unintended negative consequences of technological diffusion are given. The adoption of automatic tomato pickers developed by Midwest agricultural colleges led to the adoption of harder tomatoes (disliked by consumers) and the loss of thousands of jobs leading to the collapse of thousands of small farmers. In another example, the adoption of snowmobiles in Saami reindeer herding culture is found to lead to the collapse of their society with widespread alcoholism and unemployment for the herders, ill-health for the reindeer (such as stress ulcers, miscarriages) and a huge increase in inequality. Mathematical treatment The diffusion of an innovation typically follows an S shaped curve which often resembles a logistic function. Mathematical programming models such as the S-D model apply the diffusion of innovations theory to real data problems. In addition to that, agent-based models follow a more intuitive process by designing individual-level rules to model diffusion of ideas and innovations. Complex systems models Complex network models can also be used to investigate the spread of innovations among individuals connected to each other by a network of peer-to-peer influences, such as in a physical community or neighborhood. Such models represent a system of individuals as nodes in a network (or graph). The interactions that link these individuals are represented by the edges of the network and can be based on the probability or strength of