Sequential Decision-Making in Ants and Implications to the Evidence Accumulation Decision Model

Cooperative transport of large food loads by Paratrechina longicornis ants demands repeated decision-making. Inspired by the Evidence Accumulation (EA) model classically used to describe decision-making in the brain, we conducted a binary choice experiment where carrying ants rely on social information to choose between two paths. We found that the carried load performs a biased random walk that continuously alternates between the two options. We show that this motion constitutes a physical realization of the abstract EA model and exhibits an emergent version of the psychophysical Weber’s law. In contrast to the EA model, we found that the load’s random step size is not fixed but, rather, varies with both evidence and circumstances. Using theoretical modeling we show that variable step size expands the scope of the EA model from isolated to sequential decisions. We hypothesize that this phenomenon may also be relevant in neuronal circuits that perform sequential decisions.

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A Model of Path-Dependence in Decisions over Multiple Propositions

American Political Science Review ◽

10.1017/s0003055404001303 ◽

2004 ◽

Vol 98 (3) ◽

pp. 495-513 ◽

Cited By ~ 64

Author(s):

CHRISTIAN LIST

Keyword(s):

Decision Making ◽

Decision Process ◽

Propositional Logic ◽

Path Dependence ◽

Strategic Voting ◽

Sequential Decision ◽

Sequential Decisions ◽

Sequential Decision Process ◽

Path Dependent ◽

Necessary And Sufficient

I model sequential decisions over multiple interconnected propositions and investigate path-dependence in such decisions. The propositions and their interconnections are represented in propositional logic. A sequential decision process is path-dependent if its outcome depends on the order in which the propositions are considered. Assuming that earlier decisions constrain later ones, I prove three main results: First, certain rationality violations by the decision-making agent—individual or group—are necessary and sufficient for path-dependence. Second, under some conditions, path-dependence is unavoidable in decisions made by groups. Third, path-dependence makes decisions vulnerable to strategic agenda setting and strategic voting. I also discuss escape routes from path-dependence. My results are relevant to discussions on collective consistency and reason-based decision-making, focusing not only on outcomes, but also on underlying reasons, beliefs, and constraints.

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Optimizing sequential decisions in the drift-diffusion model

10.1101/344028 ◽

2018 ◽

Author(s):

Khanh P. Nguyen ◽

Krešimir Josić ◽

Zachary P. Kilpatrick

Keyword(s):

Decision Making ◽

Ideal Observer ◽

Natural Environments ◽

Drift Diffusion Model ◽

Sequential Decisions ◽

Drift Diffusion ◽

Evidence Accumulation ◽

Temporal Correlations ◽

Finite Sequences ◽

Decision Times

AbstractTo make decisions organisms often accumulate information across multiple timescales. However, most experimental and modeling studies of decision-making focus on sequences of independent trials. On the other hand, natural environments are characterized by long temporal correlations, and evidence used to make a present choice is often relevant to future decisions. To understand decision-making under these conditions we analyze how a model ideal observer accumulates evidence to freely make choices across a sequence of correlated trials. We use principles of probabilistic inference to show that an ideal observer incorporates information obtained on one trial as an initial bias on the next. This bias decreases the time, but not the accuracy of the next decision. Furthermore, in finite sequences of trials the rate of reward is maximized when the observer deliberates longer for early decisions, but responds more quickly towards the end of the sequence. Our model also explains experimentally observed patterns in decision times and choices, thus providing a mathematically principled foundation for evidence-accumulation models of sequential decisions.

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Students As Sequential Decision-Makers: Quantifying the Impact of Problem Knowledge and Process Deviation on the Achievement of Their Design Problem Objective

Volume 3: 20th International Conference on Advanced Vehicle Technologies; 15th International Conference on Design Education ◽

10.1115/detc2018-85537 ◽

2018 ◽

Author(s):

Murtuza Shergadwala ◽

Ilias Bilionis ◽

Jitesh H. Panchal

Keyword(s):

Decision Making ◽

Design Process ◽

Design Problem ◽

Design Space ◽

Research Question ◽

Decision Makers ◽

Sequential Decision ◽

Sequential Decisions ◽

Decision Making Model ◽

The Impact

Factors such as a student’s knowledge of the design problem and their deviation from a design process impact the achievement of their design problem objective. Typically, an instructor provides students with qualitative assessments of such factors. To provide accurate assessments, there is a need to quantify the impact of such factors in a design process. Moreover, design processes are iterative in nature. Therefore, the research question addressed in this study is, How can we quantify the impact of a student’s problem knowledge and their deviation from a design process, on the achievement of their design problem objective, in successive design iterations? We illustrate an approach in the context of a decision-making scenario. In the scenario, a student makes sequential decisions to optimize a mathematically unknown design objective with given constraints. Consequently, we utilize a decision-making model to abstract their design process. Their problem knowledge is quantified as their belief about the feasibility of the design space via a probability distribution. Their deviation from the decision-making model is quantified by introducing uncertainty in the model. We simulate cases where they have a combination of high (or low) knowledge of the design problem and high (or low) deviation in their design process. The results of our simulation study indicate that if students have a high (low) deviation from the modeled design process then we cannot (can) infer their knowledge of the design problem based on their problem objective achievement.

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