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Matthew Kelly
Matthew Kelly

The Fisher Roulette Strategy.pdf



Casino Night is McGlinn Hall's signature event held every winter. It is a night of fun "casino-style" games, with most attendees playing roulette and blackjack with play money. All of the proceeds from the event are donated by the Shamrocks to St. Adalberts, a local grade school in South Bend. McGlinn also runs a Bubble Soccer tournament, a signature event that started in the fall of 2015, and is held in the fall and spring every year. Players create a team and play soccer while in giant, inflatable bubbles, with the proceeds also going to St. Adalberts.




The Fisher Roulette Strategy.pdf


DOWNLOAD: https://www.google.com/url?q=https%3A%2F%2Furluso.com%2F2udt8n&sa=D&sntz=1&usg=AOvVaw09EdOAy81R0ncE2EdFbJ5B



We describe here a model used to investigate the factors facilitating the emergence of self-governance in this fishery. The basic premise of the model is that the relatively sedentary behavior of lobsters and the technology of their capture (traps), combined with the self-interested, competitive behavior of individual fishers create circumstances that facilitate collective action. The same people, in the same place, fishing for other species (such as scallops, urchins, groundfish, and shrimp) with mobile fishing gear did not arrive at similar self-organizing arrangements.


A fisher's challenge is to maximize income in this changing natural and human environment. The human environment is especially complex for the fisher because it requires continuous adaptation to the strategically competitive behavior of other fishers. There are two principal modes of competition used by fishers. One is comparable to what ecologists call scramble competition. Fishers need to search out and capture lobsters before other fishers. Each day a fisher might haul several hundred traps and must decide where to put each trap next. That decision is based on information (most of it very imperfect) about the activities and catch rates of other fishers, water temperature, bottom type, and a variety of other indicators about the natural environment and, of course, the fisher's own recent and longer-term experiences. To compete effectively through scramble competition, fishers closely watch and quickly adapt to the changes in the location of their own catch and to the location of the catch of other fishers.


The second mode of competition is comparable to what ecologists call interference competition. In the lobster fishery, interference competition occurs when fishers directly reduce the capabilities of their competitors by destroying, i.e., cutting, their competitors' traps. It is almost as if retailers could compete by burning one another's stores. The benefit of cutting is a reduction in competition, but there is a high risk of reciprocal action by affected competitors. Trap cutting, consequently, can be a very costly form of competition and is one that fishers try their best to avoid. It does not occur frequently; nevertheless, it is possible, and the constant threat of its occurrence is a significant restraint on fishers' activities.


The basic logic of the decision process is reasonably straightforward. The biophysical model and the agent-based model together provide the fisher with information about the current state of the environment. Each fisher has a memory that consists of a list of decision rules (classifiers) of the form described above; each rule is accompanied by a weight that reflects its performance (profitability) in previous use.


The first class is historical information about the location of lobsters and fishing patterns. This information is contained in the fisher's decision rules. It is an imperfect indicator of the current location of the resource because the environment changes both in the long term, due to changing recruitment of lobsters, and in the short term, due to the activities of other fishers. This information is useful to the fisher only to the extent that there are environmental regularities that persist despite these changes.


Observations concerning the immediate biophysical circumstances relevant to the trap the fisher just hauled and his current catch rate make up the second class of information. The fisher is assumed to be fully knowledgeable about this data.


Areas fished with no trap cutting (a) and with trap cutting (b). The map records the percent of visits of fishers from Harbor B (bottom of the island, lower center) to areas of the map: black, 100%; gray, contested area; white, 0%; green, land. Contested areas can change from run to run, depending on the spatial patterns of fishing established in the first few years of the run, but territories always develop. SI Fig. 8 shows the evolution of the groups forming these territories in a typical run of the model.


We describe two kinds of competition: scramble competition, in which fishers race to find the patchy resource, and interference competition, in which fishers destroy traps used by other fishers. In a patchy, changing environment, knowledge of the location of the resource is the key to competitive success. Fishers acquire this knowledge through costly individual search and communications with limited numbers of other fishers. The resulting patterns of information availability are the principal determinant of the social relationships developed by individual fishers and by groups of fishers.


Individual search tends to follow a pattern in which there is an initial move to a location; the resource at that location is fished down until the rate of catch is below what is perceived available elsewhere, and another move is made. As fishers search, they encounter one another. The more frequent the encounters, the more they learn about one another's fishing patterns and, consequently, the more likely they are to imitate one another. Imitation, of course, increases the frequency of contact and eventually leads to the formation of persistent groups of fishers. As members of a group, fishers gain significant knowledge of the resource; however, as more fishers rely on imitation, the advantages of being a member of a group decline because less new information is acquired. This leads to circumstances in which individuals often find it advantageous to fish away from the group. Consequently, when the group as a whole is considered, a mix of group-oriented behavior, imitation, and autonomous behavior, exploration, tends to occur. The balance between group and autonomous behavior is driven by the self-interested actions of individual fishers and is an important determinant of fleet efficiency. The groups that form as a result of scramble competition are the beginning of the social relationships important for governance. However, the activities of these groups overlap in space and, consequently, do not generate the boundaries necessary for effective collective action.


We thank the 44 Maine lobster fishers for their extensive volunteer effort collecting the data used in this study. Fishers Steve Robbins III and Ted Ames provided valuable feedback, as did our colleagues Yong Chen, Jim Fastook, Dave Hiebeler, Bonnie McCay, Jim McCleave, Geoff Shester, and Wendy Weisman. Several anonymous referees provided extensive and very helpful comments. This work was supported principally by a Maine Sea Grant and also in part by the Maine Department of Marine Resources, National Science Foundation Program Biocomplexity in the Environment Grant OCE-0410439, the Resilience Alliance, and the National Center for Ecological Analysis and Synthesis Working Group on Ocean Ecosystem-Based Management: The Role of Zoning.


Professional gamblers know that when it comes to the game of roulette, the best strategy is the same one that supercomputer Joshua applied to nuclear war in the movie WarGames: "The only way to win is not to play."


But that didn't stop a group of chaos theorists from trying to beat it anyway. And according to new research published in the journal Chaos, it looks like they may have found a way to beat the house. They were able to model the motion of the wheel and ball and were able to confirm their predictions both in simulation and using an actual roulette wheel.


Of course, casinos tend to frown on precise measurements and computer simulations by their gamblers, so the researchers developed a simpler method of predicting the outcome of a roulette game that could be deployed without notice. The first step is simply for a player to note the time it takes for the ball to pass a fixed point to get a rough approximation of the velocity of the ball. That approach, according to the researchers, produces results that " although noisy, are feasible" for making predictions.


Using those simple measurements and the equations presented in the paper, the researchers were able to predict which half of the roulette wheel the ball would end up in about 59% of the time. By betting strategically in accordance with those predictions, this enabled them to get an 18% return on their gambling. That's compared to a -2.7% return in the normal course of roulette gambling. 041b061a72


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