Learning without Recall: A Case for Log-Linear Learning

We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independent and identically distributed private signals as well as the beliefs of their neighboring agents at each time. Fully rational agents would successively apply Bayes rule to the entire history of observations. This leads to forebodingly complex inferences due to lack of knowledge about the global network structure that causes those observations. To address these complexities, we consider a Learning without Recall model, which in addition to providing a tractable framework for analyzing the behavior of rational agents in social networks, can also provide a behavioral foundation for the variety of non-Bayesian update rules in the literature. We present the implications of various choices for time-varying priors of such agents and how this choice affects learning and its rate.Comment: in 5th IFAC Workshop on Distributed Estimation and Control in Networked Systems, (NecSys 2015

Learning without Recall by Random Walks on Directed Graphs

We consider a network of agents that aim to learn some unknown state of the world using private observations and exchange of beliefs. At each time, agents observe private signals generated based on the true unknown state. Each agent might not be able to distinguish the true state based only on her private observations. This occurs when some other states are observationally equivalent to the true state from the agent's perspective. To overcome this shortcoming, agents must communicate with each other to benefit from local observations. We propose a model where each agent selects one of her neighbors randomly at each time. Then, she refines her opinion using her private signal and the prior of that particular neighbor. The proposed rule can be thought of as a Bayesian agent who cannot recall the priors based on which other agents make inferences. This learning without recall approach preserves some aspects of the Bayesian inference while being computationally tractable. By establishing a correspondence with a random walk on the network graph, we prove that under the described protocol, agents learn the truth exponentially fast in the almost sure sense. The asymptotic rate is expressed as the sum of the relative entropies between the signal structures of every agent weighted by the stationary distribution of the random walk.Comment: 6 pages, To Appear in Conference on Decision and Control 201

Transformability in Post-Earthquake Houses in Iran: with Special Focus on Lar City

Earthquake is considered as one of the most catastrophic disasters in Iran, in terms of both short-term and longterm hazards. Due to the particular financial and time constraints in Iran, quickly constructed post-earthquake houses (PEHs) do not fulfill the minimum requirements to be considered as comfortable dwellings for people. Consequently, people often transform PEHs after they start to reside. However, lack of understanding about process, motivation, and results of housing transformation leads to construction of some houses not suitable for future transformations, hence resulting in eventually demolished or abandoned PEHs. This study investigated housing transformations in a natural bed of postearthquake Lar. This paper reports results of the conducted survey for comparing normal condition housing transformation with postearthquake housing transformation in order to reveal the factors that affect post-earthquake housing transformation in Iran. The findings proposed the use of a combination of ‘Temporary’ and ‘Permanent’ housing reconstruction models in Iran to provide victims with basic but permanent post-disaster dwellings. It is also suggested that needs for future transformation should be predicted and addressed during early stages of design and development. This study contributes to both research and practice regarding post-earthquake housing reconstruction in Iran by proposing new design approaches and guidelines

Controllability and Fraction of Leaders in Infinite Network

In this paper, we study controllability of a network of linear single-integrator agents when the network size goes to infinity. We first investigate the effect of increasing size by injecting an input at every node and requiring that network controllability Gramian remain well-conditioned with the increasing dimension. We provide theoretical justification to the intuition that high degree nodes pose a challenge to network controllability. In particular, the controllability Gramian for the networks with bounded maximum degrees is shown to remain well-conditioned even as the network size goes to infinity. In the canonical cases of star, chain and ring networks, we also provide closed-form expressions which bound the condition number of the controllability Gramian in terms of the network size. We next consider the effect of the choice and number of leader nodes by actuating only a subset of nodes and considering the least eigenvalue of the Gramian as the network size increases. Accordingly, while a directed star topology can never be made controllable for all sizes by injecting an input just at a fraction $f<1$ of nodes; for path or cycle networks, the designer can actuate a non-zero fraction of nodes and spread them throughout the network in such way that the least eigenvalue of the Gramians remain bounded away from zero with the increasing size. The results offer interesting insights on the challenges of control in large networks and with high-degree nodes.Comment: 6 pages, 3 figures, to appear in 2014 IEEE CD

Minimal Actuator Placement with Optimal Control Constraints

We introduce the problem of minimal actuator placement in a linear control system so that a bound on the minimum control effort for a given state transfer is satisfied while controllability is ensured. We first show that this is an NP-hard problem following the recent work of Olshevsky. Next, we prove that this problem has a supermodular structure. Afterwards, we provide an efficient algorithm that approximates up to a multiplicative factor of O(logn), where n is the size of the multi-agent network, any optimal actuator set that meets the specified energy criterion. Moreover, we show that this is the best approximation factor one can achieve in polynomial-time for the worst case. Finally, we test this algorithm over large Erdos-Renyi random networks to further demonstrate its efficiency.Comment: This version includes all the omitted proofs from the one to appear in the American Control Conference (ACC) 2015 proceeding

Architectural changes and motivational factors for post-earthquake house transformation in Lar City, Iran

Housing provision for post-earthquake victims requires consideration of the victims’ cultural and social needs. Due to limited time and shortage of funds, quick construction of post-earthquake houses often fails to comply with the minimum needs of the occupants. Too often, such houses are either abandoned or transformed substantially, resulting in an overwhelming waste of resources. This paper aimed to investigate the transformation process of post-earthquake houses in Iran, in particular, people’s motivational factors for these transformations. The methods used included systematic observations and map analysis of alterations to post-earthquake houses over a 30-year period (1970-2000), interviews with the households and questionnaire survey with 190 respondents. The results recommended that the design for these houses should address potential for transformability into preearthquake patterns and lifestyles, adaptability to new parts/construction and capability to reflect different requirements for indoor and outdoor spatial circulations. Analysis of house transformation in the 30-year period revealed that the majority of respondents were found to favour vernacular architecture design, which includes a courtyard in transforming their houses (51.1%) followed by the desire to follow the current trend (32%). The majority of houses had undergone major transformation, having added more than 98% of the original built-up area to the houses. This research found that the mixed ‘Temporary- Permanent’ housing reconstruction model was highly successful since it involved participation of end users from the very early stages of design and development in order to predict and accommodate later housing alteration issues

Failure Analysis in Multi-Agent Networks: A Graph-Theoretic Approach

A multi-agent network system consists of a group of dynamic control agents which interact according to a given information flow structure. Such cooperative dynamics over a network may be strongly affected by the removal of network nodes and communication links, thus potentially compromising the functionality of the overall system. The chief purpose of this thesis is to explore and address the challenges of multi-agent cooperative control under various fault and failure scenarios by analyzing the network graph-topology. In the first part, the agents are assumed to evolve according to the linear agreement protocol. Link failures in the network are characterized based on the ability to distinguish the agent dynamics before and after failures. Sufficient topological conditions are provided, under which dynamics of a given agent is distinguishable for distinct digraphs. The second part of this thesis is concerned with the preservation of structural controllability for a multi-agent network under simultaneous link and agent failures. To this end, the previously studied concepts of link and agent controllability degrees are first exploited to provide quantitative measures for the contribution of a particular link or agent to the controllability of the overall network. Next, the case when both communication links and agents in the network can fail simultaneously is considered, and graphical conditions for preservation of controllability are investigated

Transformability in post-earthquake houses in Iran: with special focus on Lar City

Earthquake is considered as one of the most catastrophic disasters in Iran, in terms of both short-term and longterm hazards. Due to the particular financial and time constraints in Iran, quickly constructed post-earthquake houses (PEHs) do not fulfill the minimum requirements to be considered as comfortable dwellings for people. Consequently, people often transform PEHs after they start to reside. However, lack of understanding about process, motivation, and results of housing transformation leads to construction of some houses not suitable for future transformations, hence resulting in eventually demolished or abandoned PEHs. This study investigated housing transformations in a natural bed of postearthquake Lar. This paper reports results of the conducted survey for comparing normal condition housing transformation with postearthquake housing transformation in order to reveal the factors that affect post-earthquake housing transformation in Iran. The findings proposed the use of a combination of 'Temporary' and 'Permanent' housing reconstruction models in Iran to provide victims with basic but permanent post-disaster dwellings. It is also suggested that needs for future transformation should be predicted and addressed during early stages of design and development. This study contributes to both research and practice regarding post-earthquake housing reconstruction in Iran by proposing new design approaches and guidelines